Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3682 2018 2018 Associate Professor, Department of Computer Science and Engineering, Vignan Institute of Technology and Science, Hyderabad, India Udit Udit Professor, Department of Information Technology, Institute of Technology & Science, Ghaziabad, Uttar Pradesh, India Sunil Kr Pandey Associate Professor, Department of Electronics and Computer Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, India Shailaja Mantha Assistant Professor, Department of CSE (AI & ML), Manipal University Jaipur, Dahmi-Kalan, Jaipur, Rajasthan, India Priya Goyal Assistant Professor, Department of CSE (AI & ML), Nawab Shah Alam Khan College of Engineering and Technology, Hyderabad, India Asmath Jabeen Associate Professor, Department of Computer Science, Nawab Shah Alam Khan College of Engineering and Technology, Telangana, India Shameem Fatima Associate Professor, Faculty of Engineering & Technology, Poornima University, Jaipur, India Udit Mamodiya The complex developing nature of urban infrastructure necessitates intelligent solutions for optimizing smart city operations. Based on this research paper, a multi-modal fusion framework that integrates real-time traffic and environmental sensor data with advanced machine learning algorithms to enhance decision-making for urban traffic management and pollution control is proposed. A hybrid AI model is proposed, with a combination of CNNs for the estimation of image-based traffic density, LSTM networks for the time-series environmental prediction, and RL for adaptive control of traffic signals. The system proposed integrates sensor data in real-time from cameras, GPS, LiDAR, and nodes for environmental monitoring to create an optimized control strategy. The model has been deployed on edge computing devices, such as Raspberry Pi, to enable the real-time processing and reduce the latency. Security layer based on block chain for data integrity protection and tamper proofing within smart city networks. The suggested system shows high improvements in congestion reduction, better accuracy in air pollution forecasting, and energy efficiency in urban management. It will be validated using simulation with SUMO and MATLAB and real-world sensor data that the sensor fusion approach outperforms the conventional fixed-rule strategies of traffic management. This work allows for cost-effective, large-scale smart city deployment that would reduce traffic delay and urban air pollution while securing data and being computationally efficient. The low-latency decision-making approach with edge-AI makes it fit for real-time urban governance. Unlike traditional models that process either traffic or environmental data in silos, the work presented herein integrates multi-source sensor data with edge computing and blockchain security for a unified AI-driven fusion approach, thus building a robust framework for next-generation smart city intelligence. 2025 2025 328 340 10.54216/FPA.190224 https://www.americaspg.com/articleinfo/3/show/3682