The rapid advancement of technology has significantly improved environmental monitoring and wildlife conservation; however, forest authorities and wildlife researchers still face challenges in tracking animal movements and preventing illegal activities such as poaching. Traditional monitoring methods like manual forest patrols, basic camera traps, and periodic observations are often inefficient, inconsistent, and lack predictive capabilities, leading to missed wildlife data and delayed responses to ecological threats. To address these challenges, this project proposes a Smart Wildlife Monitoring System using Machine Learning and Data Analytics, a web-based intelligent platform designed to monitor, identify, and analyse wildlife activity efficiently. The system allows users to upload captured image datasets or record wildlife observations such as animal type, location, detection time, environmental conditions, and movement patterns through a secure interface. By applying machine learning algorithms and statistical analysis techniques, the system performs automated animal classification, wildlife activity prediction, and behavioural trend analysis to support better decision-making for conservation authorities. Furthermore, the system is scalable for future enhancements such as IoT sensor integration and advanced deep learning models, contributing to smarter, technology-driven, and sustainable wildlife conservation and ecosystem management.