Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/1712 2018 2018 Al-Turath University College, Baghdad, 10021, Iraq Mohammed A. J. Maktoof Department of Computer Techniques Engineering, Al-Rafidain University College, Baghdad 10064, Iraq Ibraheem H.. M. Department of Computer Techniques Engineering, Mazaya University College, Thi Qar, Iraq Mohammed A. Abdul Razzaq Department of Medical instruments engineering techniques, Alfarahidi University, Baghdad, Iraq Ahmed Abbas Department of Buildings and Construction Techniques Engineering, Al-Mustaqbal University College, 51001 Hillah, Babylon, Iraq Ali Majdi The proposed method of using Machine Learning in Motion Detection and Pedestrian Tracking-assisted Intelligent Video Surveillance Systems (ML-IVSS) can be seen as an application of intelligent fusion techniques. ML-IVSS combines the power of motion detection, pedestrian tracking, and machine learning to create a more accurate and efficient surveillance system for smart cities. By fusing these techniques, ML-IVSS can effectively detect unusual behaviors such as trespassing, interruption, crime, or fall-down, and provide accurate depth data from surveillance footage to protect residents. Intelligent fusion techniques can help improve the accuracy and effectiveness of surveillance systems in smart cities, making them safer and more secure for residents. Combination channel models are used at first, and an object area with prominent features is selected for surveillance. Scaled modification and extraction of features are carried out on the presumed object's region. Identifying the low-level characteristic is the first step in incorporating it into neural architectures for deep feature learning. A smart CCTV data set is used to evaluate the proposed method's performance. According to the numerical analysis, the proposed ML-IVSS model outperforms other traditional approaches in terms of abnormal behaviour detection (98.8%), prediction (97.4%), accuracy (96.9%), F1-score (97.1%), precision (95.6%), and recall (96.2%). 2023 2023 35 47 10.54216/FPA.110203 https://www.americaspg.com/articleinfo/3/show/1712