AI in Urban Planning Market By Component (Solutions {AI Software for Urban Simulation, Geospatial AI Platforms, Smart Zoning & Land Use Tools, Predictive Modeling Software, Others}, Services {Consulting & Strategy Services, Integration & Implementation, Training & Support, Others}), By Application (Smart Infrastructure Planning, Traffic and Transportation Management, Energy & Utility Planning, Land Use, Environmental Monitoring, Disaster Management, Others), By Technology (Machine Learning, Deep Learning, Computer Vision, Natural Language Processing (NLP), Predictive Analytics, Digital Twin Technology, Others), By Data Source (Satellite Imagery, Aerial Drones, CCTV and Traffic Cameras, Mobile GPS Data, IoT Sensors, Others), and By End User (Government & Municipalities, Urban Development Authorities, City Planners & Architects, Smart City Technology Vendors, Real Estate Developers, Others), Global Market Size, Segmental analysis, Regional Overview, Company share analysis, Leading Company Profiles And Market Forecast, 2025 – 2035

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What trends will shape AI in Urban Planning Market in the coming years?

The AI in Urban Planning Market accounted for USD 1.89 Billion in 2024 and USD 2.26 Billion in 2025 is expected to reach USD 13.60 Billion by 2035, growing at a CAGR of around 19.65% between 2025 and 2035. Machine learning, computer vision, and predictive analytics are examples of artificial intelligence (AI) in the urban planning market that improve the process of creating and developing cities and their management. Planners can now effectively use charts, work with massive datasets, model urban expansion, and improve sustainability due to artificial intelligence. It can help make better decisions since it can be used to model zoning, infrastructure, and environmental impact. Those tools, platforms, and consulting services used by governments, urban developers, and smart city projects are present in the market. The rising discipline focuses on the development of livelier, efficient, and resilient cities.

What do industry experts say about the AI in Urban Planning market trends?

“AI-Enabled Urban Planning: Strategies for Sustainable and People-Centered Cities”

• Dr. Mark van Rijmenam, Futurist & Strategic Speaker on Smart Cities

“Using AI-enabled spatial and data analytics helps us… optimise land use, improve citizens’ accessibility to services… and manage utilisation of infrastructure.”

• Huang Zhongwen, Director, Design & Planning Lab, Singapore URA

Which segments and geographies does the report analyze?

What are the key drivers and challenges shaping the AI in Urban Planning market?

How can data analytics revolutionize urban growth decision-making?

Analytics in data has the capability of transforming decision-making in urban growth, where it will allow city planners and policymakers to make more economical decisions that are informed and sustainable. Analytics can be used to figure out patterns, look ahead into the future, and decide how cities will be able to meet their needs using the large quantities of data, everything from traffic flows to population demographics to energy efficiency and the environmental impacts thereof. The U.S. Census Bureau predicts that the urban population will rise by 20% points within the following decade, putting extra pressure on the infrastructure and services. Driven by data, cities can become proactive even regarding such issues as traffic jams, lack of accommodation, and environmental aspects.

The National Institute of Standards and Technology (NIST) recommends that the integration of data analytics in urban planning would save up to 15% of the infrastructure costs and further respond to emergencies up to 25% faster. Moreover, IoT sensors can increase the safety of people and affect public safety and environmental monitoring to make more intelligent development strategies. Overall, data analytics enables cities to develop in a manner that strikes a balance between economic growth and quality of life and promotes sustainability, adaptability, or resilience of urban change.

Can predictive modeling reduce congestion and optimize land use?

In urban planning, predictive modelling can be vital in minimising congestion and streamlining land utilisation, mainly because it utilises data-driven predictive analysis to estimate the transportation and population trends and infrastructure demands. Predictive models can be used to inform city planners how to create a more efficient transportation network and distribute land resources by analysing the past and real-time data.

For Instance, the Department of Transportation of the U.S. reveals the power of predictive analytics to alleviate traffic congestion by as much as 20 percent by optimising signals and routes. Further, observations as reported by the Urban Institute reveal that predictive tools have the capacity to aid in the forecasting of urban sprawl in order to be able to facilitate sounder zoning choices to support sustainable land use. AI-driven predictive modelling integrations into the process of smart urban development will minimise the consequences it has on the environment and increase the quality of living of the residents, as they will spend fewer hours in transit and will not be overcrowded.

The method suits objectives proposed by the National Institute of Standards and Technology (NIST), which promotes data-based planning as the key to establishing resilient and adaptable cities. Overall, predictive modelling can be a great force in the AI urban planning market that allows making more informed decisions by looking at both development and liveability.

Are legacy systems hindering integration of intelligent technologies?

The major downfall of legacy systems is that they limit AI implementation in urban planning processes because the infrastructure that consists of legacy systems lacks compatibility with newer systems and may have outdated data formats. As a 2023 report on the UK House of Commons Public Accounts Committee suggests, 28% of the IT systems of the central government were classified as legacy ones, and the majority were a direct impediment to modernisation, with ex-matters, such as the implementation of intelligent planning tools, being part of the problem.

The National Informatics Centre (NIC) has identified legacy systems within the local governance sector as a hindrance towards realising a real-time rollout of smart urban solutions such as real-time waste management tracking or AI-based zoning analysis, among others. Further, MIT researchers in the Urban Studies and Planning department revealed that legacy data structure is a hindrance to AI applications, especially in a situation where a city makes use of scattered GIS systems and does not have a dynamic data set. Such problems add to the expenses, impede the development of smart infrastructure, and decrease the efficacy of AI algorithms that require active and high-quality data feeds. So, the full potential of AI in urban planning cannot be achieved until differences between legacy systems and modern systems in the urban planning sphere are resolved.

Does autonomous mobility present new models for zoning development?

Autonomous mobility offers new ways of zoning development due to its ability to realise more flexible and efficient patterns of land use and minimise the requirements of traditional parking and road networks. Since the transport system of autonomous vehicles (AVs) can make owning a private car a luxury, urban planners can invest that space, which is used as a gigantic parking lot, in mixed-use construction or parks. According to research by the U.S. Department of Energy (DOE), AVs would perhaps see a 90 percent decrease in parking in metropolitan locations.

The City Science group at MIT has conducted a study into how dynamic zoning could respond to changes in transportation patterns projected with AVs to result in walkable and transit-oriented communities. Autonomous shuttles and deliveries contribute to dense urban cores, less traffic congestion, and the stimulation of growth in vertical mixed-use. These changes will enable more dynamic zoning policies that react to the immediacy of mobility data to lead to a more sustainable and equitable distribution of services.

Will green city designs benefit from AI-driven simulations?

Green cities planning can gain a lot by simulations promoted with the help of AI because it helps planners to model the ecological influences, maximise energy consumption, and forecast urbanisation with an enhanced level of accuracy. By using AI tools, cities are able to simulate how building air flow, sunlight exposure, and drainage are affected, therefore allowing cities to have sustainable infrastructure even before construction has started.

According to the U.S. Department of Energy's National Renewable Energy Laboratory (NREL), it was found that energy modelling using AI can be used to cut down energy used in buildings by up to 30% through effective design options. The Senseable City Lab at MIT has demonstrated how AI and real-time data could advance city green space design to improve population health and minimise the effects of urban heat islands. These strengths enhance environmental performance and assist governments in making more efficient resource allocations, and these make AI a very important resource to achieve resilient and sustainable cities.

What are the key market segments in the AI in Urban Planning industry?

Based on the component, the AI in Urban Planning Market is classified into Solutions and Services. In the AI in Urban Planning market, the solutions are in the dominant segment. The reason is that urban planning makes extensive use of sophisticated solutions in AI-powered software products like predictive analytics, simulation models, and geographic information systems (GIS) to streamline urban planning, traffic, and resource distribution in cities. Such AI solutions allow planners to make evidence-based decisions, make cities more sustainable, and have more efficient urban infrastructure. The main value and market desire revolve around these new AI solutions, but implementation and customisation take place with the help of services.

Based on the application, the AI in Urban Planning Market is classified into Smart Infrastructure Planning, Traffic and Transportation Management, Energy & Utility Planning, Land Use, Environmental Monitoring, Disaster Management, and Others. Traffic and transportation management is the most noticeable application segment of AI in urban planning market. As urban populations are increasing very fast, it is essential to deal with traffic jams and make the best use of the available transport systems and efficient mobility, which can be solved by the use of AI. Predictive traffic, real-time route planning, and self-driving vehicles enable shorter travel times and lower carbon-emission levels, and the overall liveability of cities. This emphasis renders traffic and transportation management the leading application that has been generating demand in the AI urban planning industry.

Which regions are leading the AI in Urban Planning market, and why?

The North American AI in urban planning market is leading due to its strong technological environment, robust AI-based startup culture, and significant investments from the public and private sectors. It is observed that the active implementation of smart airport projects is going on in the cities of the U.S. and Canada, where AI is applied to smarten up traffic conditions, energy use, waste management, and mobility. The area is supported by sturdy research institutions and tech powerhouses such as Google, IBM, and Microsoft that are engaged intensively in urban analytics and AI advancement.

The government provides further boosts through funding and policy structures to AI use in urban planning. The elevated urbanisation rates and the increased interest in sustainability and liveability are pressuring municipalities to make use of AI to plan the city rationally, using data as it is needed. The presence of high-quality data sets and close partnership between research institutes and the industry is also another factor that makes North America ahead in this area.

The Asia Pacific AI in urban planning market is developing rapidly due to intensive urbanisation, smart city projects launched by the governments, and the developments in technology infrastructure. The first among them includes countries such as China, Japan, South Korea, and Singapore, which have been using AI to solve various issues such as alleviating traffic congestion, pollution management, and effective land use. The population density and the growing need for sustainable urban planning are promoting the use of AI-based planning tools.

Innovation takes place faster due to the high level of digitalisation in the region and the availability of large and small technology businesses. The presence of active government policies and the partnerships between the government and the population are creating an accommodating environment to implement AI in urban planning. Asia Pacific is equally blessed with a youthful, tech-savvy culture with a thriving middle class, a factor that further drives the need for smart and livable cities.

What does the competitive landscape of the AI in Urban Planning market look like?

The competitive environment of AI in the urban planning market is due to the entries of the long-established technology businesses and fairly new startups. The same large actors, such as Autodesk, Esri, IBM, Siemens, and Microsoft, are enhancing their practice in urban-centric AI by incorporating geospatial information, predictive modelling, and digital twin technology in urban-planning software. Siemens bought a smart-city software company in the pursuit of more AI-based infrastructure planning, and Microsoft is gearing up its Azure-based urban analytics toward sustainability-oriented projects.

The startups on the affordable end, such as UrbanistAI and Felt, are already getting noticed, proposing AI-based participatory planning products and climate risk mapping. Felt is attracting more capital, perhaps indicative of increased investor faith in AI platforms that are specifically focused on urban problems, including flood modelling and land-use planning. This active environment is characterised by the integration of dimensions in scale, specialisation, and strategic innovation.

AI in Urban Planning Market, Company Shares Analysis, 2024

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Which recent mergers, acquisitions, or product launches are shaping the AI in Urban Planning industry?

• In July 2025, in Lucknow, India, MLA Rajeshwar Singh proposed an AI-based Intelligent Traffic Management System (AI-ITMS) to the State Chief Minister. The system aimed to reduce traffic delays and vehicle emissions. It used AI-powered city monitoring to improve traffic flow. The proposal focused on making city transportation more efficient and eco-friendlier.

Report Coverage:

By Component

• Solutions
  • AI Software for Urban Simulation
  • Geospatial AI Platforms
  • Smart Zoning & Land Use Tools
  • Predictive Modeling Software
  • Others
• Services
  • Consulting & Strategy Services
  • Integration & Implementation
  • Training & Support
  • Others

By Application

• Smart Infrastructure Planning
• Traffic and Transportation Management
• Energy & Utility Planning
• Land Use
• Environmental Monitoring
• Disaster Management
• Others

By Technology

• Machine Learning
• Deep Learning
• Computer Vision
• Natural Language Processing (NLP)
• Predictive Analytics
• Digital Twin Technology
• Others

By Data Source

• Satellite Imagery
• Aerial Drones
• CCTV and Traffic Cameras
• Mobile GPS Data
• IoT Sensors
• Others

By End User

• Government & Municipalities
• Urban Development Authorities
• City Planners & Architects
• Smart City Technology Vendors
• Real Estate Developers
• Others

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:

• Autodesk, Inc.
• Bentley Systems, Incorporated
• Esri
• Siemens AG
• Google LLC
• IBM Corporation
• Microsoft Corporation
• Cisco Systems, Inc.
• Hitachi
• NEC Corporation
• Mundys
• Cambridge Systematics, Inc.
• UrbanFootprint
• UrbanistAi
• DeepBlock

Frequently Asked Questions (FAQs)