Artificial Intelligence in Animal Health Market By Component (Software, Hardware, Services), By Technology (Machine Learning {ML}, Natural Language Processing {NLP}, Computer Vision, Deep Learning, Predictive Analytics, IoT Integration, Others), By Application (Disease Diagnosis and Detection, Precision Livestock Farming, Veterinary Imaging Analysis, Animal Nutrition Optimization, Drug Discovery and Development, Genomic Analysis & Breeding Optimization, Livestock Management, Others), By Animal Type (Companion Animals, Livestock Animals), By End User (Veterinary Clinics & Hospitals, Animal Research Institutes, Livestock Farms, Pet Care Centers, Pharmaceutical & Biotech Companies), Global Market Size, Segmental analysis, Regional Overview, Company share analysis, Leading Company Profiles And Market Forecast, 2025 – 2035

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Industry Outlook

The Artificial Intelligence in Animal Health market accounted for USD 1.49 Billion in 2024 and USD 1.81 Billion in 2025 is expected to reach USD 12.87 Billion by 2035, growing at a CAGR of around 21.65% between 2025 and 2035. AI enhances animal health by enabling accurate diagnostics, real-time monitoring, and data-driven veterinary decisions. The AI in animal health market consists of the incorporation of AI technologies into the veterinary care and animal health management area. AI helps with diagnostics, treatment plans, disease prevention, and animal welfare monitoring. Modern AI systems perform predictive health management by analyzing big data retrieved from sensors, wearables, and medical records. This technology assists in drug discovery and herd management and increases operational efficiencies for veterinary clinics and farms while providing a cost-effective and scalable solution to the market. 

Report Scope:

Market Dynamics

Enhanced disease diagnosis through automated machine learning-based algorithms

The growing demand for efficient livestock management and early disease detection is fueling AI adoption in animal health. Farmers and vets increasingly must be able to use AI tools in real-time monitoring, diagnostics, and predictive analytics to assess outbreaks faster and improve animal welfare. AutoML algorithms study large amounts of data on animal health and precipitate more rapid and reliable diagnoses. Therefore, veterinary medicine needs to use more sophisticated technology to address the growing concerns about zoonoses and food safety.

Higher standards for disease surveillance and precision cattle farming are strengthened by the combination of wearable sensors, imaging technologies, and artificial intelligence. According to the USDA, animal diseases cause billions of dollars' worth of damage to the global economy each year, but early intervention programs can significantly reduce these losses. This renewed interest in AI veterinary diagnostic tools also finds academic backing, with institutions such as the University of Edinburgh’s Royal (Dick) School of Veterinary Studies actively investigating such facets. All of these factors indicate that AI presents a robust and sustained growth prospect for the animal health sector.

Increasing veterinary demand for predictive analytics and data insights

One of the main forces for the expansion of AI in the field of animal health is the rising need for data insights and predictive analytics in veterinary practice. As pet ownership and livestock production rise, veterinarians and farmers look for effective ways to track health, identify illness early, and create the best possible treatment plans.

Massive volumes of clinical, behavioral, and environmental data are processed by AI to identify trends and predict health risks for early interventions. The availability of data for AI models is also facilitated by developments in smart sensing, electronic health records, and animal wearable technologies. Further, the increased costs of veterinary care drive the use of AI to minimize diagnostic errors and improve treatment outcomes.

Pharmaceutical companies use AI in drug development and vaccine research as well. Increased awareness of animal welfare thus contributes to the growth in precision health supported by AI tools. The regulatory bodies are also inclined towards applications of data-driven solutions, encouraging further innovations in this field. Integration of AI also improves herd management, breeding programs, and supply chain processes in the livestock sector.

Advancements In Non-Invasive And Minimally Invasive Sensor Technologies Offer Opportunities To Develop More Comfortable And User-Friendly CGM Systems.

Constraining factors for the adoption of AI in animal health market include the limited technical expertise of the veterinary professionals and animal caregivers. Most veterinarians and their staff cannot be trained to understand, operate, or rely on AI-based tools and diagnostic systems. This lack of digital literacy can result in underuse of a potentially high-tech opportunity for diagnosis, treatment planning, and animal monitoring. Small setups or rural practices might be on the wrong side of this digital divide due to the lack of resources for training or upgrading infrastructure.

Resistance to change, concerns regarding data privacy, and fear of job displacement contribute to the hesitation. AI solutions could suffer slow integration, limiting their time in impacting animal healthcare outcomes. Moving ahead, user-friendly interfaces will need to be supported with targeted educational programs to bridge this skills gap to ensure enhanced AI adoption and move veterinary professionals toward precise, data-driven care. Without focused efforts in training and awareness, the full potential of AI in animal health will remain untapped.

Growing adoption of precision farming in animal husbandry systems

The growing trend in animal husbandry toward precision agriculture presents a significant opportunity for artificial intelligence in animal health market. AI's capacity to identify illnesses early and avert deaths is enhanced by real-time health monitoring using sensors and smart wearables. Predictive analytics driven by AI can help farmers anticipate outbreaks and take action to protect the health of their herds. Reproduction cycles, feeding regimens, and behavioral modifications provide AI with additional outputs that are all tailored to improve animal welfare and productivity.

Better decision-making and tailored treatment are made possible by the integration with big data systems. These developments result in higher yields, better agricultural efficiency, and lower veterinarian expenses. AI offers large-scale solutions to lessen environmental effects while taking animal welfare into account, due to the development of sustainable reasoning. AI-based diagnostic technologies assist veterinarians in carrying out quick and accurate diagnostics. All things considered, AI transforms animal health management into a responsive, effective, and data-based approach.

Integration of AI with blockchain for supply chain transparency

The integration of AI with blockchain imparts the very opportunity to maximally augment supply chain transparency, particularly in areas like animal health. AI can predict disease outbreaks by analyzing large data sets from IoT methods, veterinary records, and logistics, and monitor livestock health in real-time while optimizing resource allocation. With blockchain, this data attains an immutable and traceable status, thus building trust and accountability in the supply chain. From verifying the origin and handling of animal products to the distribution of vaccines, this combined technology assures consumer confidence and regulatory conformance.

AI can be used to detect and raise alerts on suspicious or fraudulent activities faster, while blockchain provides a decentralized ledger to record and corroborate these findings. This interaction facilitates early interventions, lowers costs, and improves animal welfare results. Overall, the AI-blockchain combination can drastically change how stakeholders, from farmers to pharmaceutical companies, build trust and verify all levels of the animal health life cycle while empowering involved actors to make better decisions in real time based on historical data. As the regulatory pressure mounts, it is a perfect opportunity to be ahead of the curve with such systems in service to meet regulatory standards as well as protect public health.

Industry Experts Opinion

"AI is the final layer of intuitive intelligence providing even higher accuracy, faster results, and insights to guide treatment decisions for the veterinary team, all while giving them time back in their workday."

• Jonathan Ayers, Chairman and CEO at IDEXX Laboratories.

"The integration of AI into veterinary medicine not only advances technology but also fosters collaboration across disciplines. The development of AI tools requires input from veterinarians, data scientists, and engineers to ensure that these tools are both effective and practical in clinical settings."

• Dr. Jasmeet Kaler, Associate Professor at the University of Nottingham School of Veterinary Medicine and Science

Segment Analysis

Based on the Technology, the Artificial Intelligence in Animal Health market has been classified into Machine Learning {ML}, Natural Language Processing {NLP}, Computer Vision, Deep Learning, Predictive Analytics, IoT Integration, and Others. The artificial intelligence in animal health market is where machine learning (ML) is most prominent among all the technologies in this field. ML generates predicted insights into the propagation of an outbreak, animal behavior, and treatment options by sifting through enormous volumes of medical data from other planets. ML shows the development of personalized animal care, diagnostics, and precision medicine through advancements based on patterns found. It is given the credit it deserves for the advancement and innovation of the industry because of its applications, which also include better livestock management, early disease identification, and veterinary process optimization.

Based on the application, the Artificial Intelligence in Animal Health market has been classified into Disease Diagnosis and Detection, Precision Livestock Farming, Veterinary Imaging Analysis, Animal Nutrition Optimization, Drug Discovery and Development, Genomic Analysis & Breeding Optimization, Livestock Management, and Others. One of the most widely applied applications in the AI for Animal Health market is disease diagnosis and detection. AI-based applications, including those using machine learning and computer vision, facilitate the early and accurate detection of animal diseases, sometimes even preceding the actual manifestation of symptoms. Such an application helps check the spread of infection and thus ensures better therapy and reduced veterinary costs. AI solutions may use analysis of medical images, observe animal behavior, or ingest data from sensors to spot abnormalities, hence making the management of diseases more effective and timely. Such measures contribute to the improvement of animal welfare and health management practices.

Regional Analysis

The regional landscape of the United States artificial intelligence in animal health market continues to grow substantially. AI technologies find applications across multiple veterinary domains, including diagnosis and disease forecasting, with treatment plans and pharmaceutical discoveries. Unprecedented AI growth occurs because of rising demands to manage animals efficiently and accurately throughout the entire veterinary industry, including livestock operations, pets, and wild animals. This market growth benefits from new machine learning technologies combined with deep learning and data analytics capabilities that speed up disease identification and strengthen animal health surveillance and decision-support systems. Leading companies in biotech, together with healthcare and tech sectors, have joined forces with veterinarians to develop AI-based workflow solutions to increase the effectiveness of animal medical services. The U.S. leads global innovation in animal health artificial intelligence because the market receives support from public and private investment funding.

The Asia Pacific Artificial Intelligence (AI) in animal health market continues to exhibit significant growth because Asian governments pour resources into technological developments while simultaneously facing expanding requirements for advanced veterinary healthcare systems. Three Asian leaders, China, India, and Japan, lead this demand surge because they use AI-driven tools to identify diseases while using AI to prevent disease outbreaks, and treat animals. AI tools, including predictive analytics, together with imaging methods and data analysis systems, are transforming the detection and management of diseases in companion animals and livestock.

The region's growing farming industry, together with rising attention to animal welfare, is driving demand for new solutions that improve both productivity and health results. Leading AI technology companies, together wiath research institutions operating in the area, are triggering additional advancement while generating new opportunities for startup companies and technology stalwarts. The market demonstrates rapid growth potential because the adoption of AI continues to rise while regulatory frameworks develop further. A set of obstacles, including data privacy issues and insufficient infrastructure, along with a scarcity of skilled workers, continues to persist.

Competitive Landscape

The competitive landscape of the AI in animal health market is evolving at a rapid pace with greater concentration on precision farming, livestock monitoring, and disease prevention. At the forefront are companies such as Connecterra, Cainthus, and Virovet, which are harnessing machine learning and computer vision for animal welfare and productivity enhancement. With their continued research on the latest monitoring solutions for livestock, Moocall persists in furthering its innovations, while Smartbell designs wearable devices for calf development tracking.

Recently, Cainthus was acquired to expand its capabilities in smart farming solutions. Similarly, FarmSee and Chirrup AI recently took steps ahead with AI implementation in real-time livestock-and-farm analytics, while Regrowth advances livestock nutrition. These advances in real-time diagnostics, herd management, and farm optimization will pave the road so that AI becomes an integral part of animal health in the future. Gradually, as the adoption of AI grows, companies are even entering into partnerships and collaborations to bring a superior level of scalability and efficiency to the deployment of their solutions in diversified global markets. Further, the increasing demand for sustainable and efficient farming practices plays into the ascension of AI-led technologies in animal health.

Artificial Intelligence in Animal Health Market, Company Shares Analysis, 2024

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Recent Developments:

• In May 2025, Mars introduced a set of AI-driven digital health tools designed to help pet owners monitor their pets' health using smartphone photos. These tools focused on assessing dental health and other wellness indicators, representing a major advancement in preventive pet care.
• In April 2025, Colossal Biosciences achieved a groundbreaking milestone by resurrecting the extinct dire wolf using advanced genetic engineering and AI. By editing the genes of modern gray wolves, they successfully birthed three pups—Romulus, Remus, and Khaleesi—representing a major achievement in de-extinction efforts.

Report Coverage:

By Component

• Software
• Hardware
• Services

By Technology

• Machine Learning (ML)
• Natural Language Processing (NLP)
• Computer Vision
• Deep Learning
• Predictive Analytics
• IoT Integration
• Others

By Application

• Disease Diagnosis and Detection
• Precision Livestock Farming
• Veterinary Imaging Analysis
• Animal Nutrition Optimization
• Drug Discovery and Development
• Genomic Analysis & Breeding Optimization
• Livestock Management
• Others

By Animal Type

• Companion Animals
• Livestock Animals

By End User

• Veterinary Clinics & Hospitals
• Animal Research Institutes
• Livestock Farms
• Pet Care Centers
• Pharmaceutical & Biotech Companies

By Region

North America

• U.S.
• Canada

Europe

• U.K.
• France
• Germany
• Italy
• Spain
• Rest of Europe

Asia Pacific

• China
• Japan
• India
• Australia
• South Korea
• Singapore
• Rest of Asia Pacific

Latin America

• Brazil
• Argentina
• Mexico
• Rest of Latin America

Middle East & Africa

• GCC Countries
• South Africa
• Rest of Middle East & Africa

List of Companies:

• Carelogy
• InFarm
• Scribenote
• Biofractal
• Chirrup AI
• Regrowth
• RumenAI
• Smartbell
• Virovet
• OneCup AI
• FarmSee
• FarmTrace
• Connecterra
• Moocall
• Cainthus

Frequently Asked Questions (FAQs)