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Livestock Health Plays A Critical Role In Ensuring Sustainable Dairy And Meat Production, Yet Early Detection Of Sickness In Cows Remains A Major Challenge For Farmers. Traditional Methods Of Disease Monitoring Rely On Manual Observation Of Behavioral And Physical Changes, Which Are Often Subjective, Time-consuming, And Prone To Human Error. With Advancements In Artificial Intelligence, Machine Learning (ML) And Deep Learning (DL) Techniques Provide Promising Solutions For Automated Health Monitoring. This Study Proposes An Intelligent System For The Early Detection Of Sickness In Cows By Analyzing Behavioral Patterns, Physiological Parameters, And Visual Features Extracted From Images And Sensor Data. Various ML Algorithms, Including Support Vector Machines (SVM) And Random Forest, Are Compared With State-of-the-art DL Models Such As Convolutional Neural Networks (CNNs) And Long Short-Term Memory (LSTM) Networks For Time-series Data. The Results Demonstrate That Deep Learning Models Achieve Superior Accuracy In Identifying Subtle Patterns Of Illness, Enabling Timely Intervention And Reducing Economic Losses. The Proposed Approach Highlights The Potential Of AI-driven Livestock Monitoring Systems To Support Farmers In Enhancing Animal Welfare, Optimizing Productivity, And Ensuring Food Security.