Urban analytics refers to the systematic use of data, technology, and analytical techniques to understand, plan, and manage urban environments more effectively. It involves collecting, processing, and analyzing vast amounts of information from various sources, including sensors, social media, administrative records, satellite imagery, and mobile devices. The primary goal of urban analytics is to support evidence-based decision-making for sustainable urban development, improve the quality of life for residents, optimize resource allocation, and enhance the efficiency of city services. By integrating data-driven insights into urban planning, urban analytics helps cities anticipate challenges, identify patterns, and design solutions that are responsive to the needs of a dynamic and growing urban population. In the context of sustainable development, urban analytics plays a critical role in achieving the United Nations’ Sustainable Development Goals (SDGs), particularly those related to sustainable cities and communities, climate action, and inclusive economic growth. One of the fundamental aspects of urban analytics is its reliance on diverse and complex data sources. Cities generate enormous volumes of data daily, ranging from traffic flow measurements, energy consumption statistics, pollution levels, demographic patterns, economic activity, public health records, and citizen feedback. By integrating these heterogeneous datasets, urban analytics creates a comprehensive picture of urban dynamics, enabling planners and policymakers to understand trends, correlations, and potential areas of intervention. Advanced technologies such as Geographic Information Systems (GIS), big data platforms, Internet of Things (IoT) devices, artificial intelligence, and machine learning algorithms form the backbone of urban analytics. These tools allow cities to process large-scale data in real time, generate predictive models, and simulate scenarios that inform strategic urban planning decisions. A salient feature of urban analytics is its ability to improve urban mobility and transportation systems. Traffic congestion, inadequate public transport, and inefficient route planning are common challenges faced by urban areas. By analyzing real-time traffic data from sensors, GPS devices, and mobile applications, urban analytics can identify congestion hotspots, optimize traffic signal timings, predict peak travel periods, and suggest alternative routes for commuters. Smart mobility solutions, such as ride-sharing platforms, autonomous vehicles, and integrated public transit networks, are increasingly guided by insights derived from urban analytics. Moreover, transportation data analytics contributes to sustainable development by reducing fuel consumption, minimizing carbon emissions, and promoting efficient urban mobility, which aligns with environmental sustainability goals. Urban analytics also plays a pivotal role in resource management and sustainability.