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Fish Detection And Tracking In Aquaculture Environments Is A Crucial Task For Monitoring Fish Behavior, Estimating Population Density, And Optimizing Farm Management Practices. Traditional Manual Observation Methods Are Labor-intensive, Prone To Error, And Lack Scalability In Large-scale Fish Farms. Recent Advancements In Computer Vision And Deep Learning Provide Automated Solutions For Real-time Monitoring. This Study Proposes An Efficient Fish Detection And Tracking Framework That Integrates The YOLO (You Only Look Once) Object Detection Algorithm With Euclidean Distance-based Tracking. YOLO Is Employed To Accurately Detect Fish In Underwater Video Streams, Leveraging Its High-speed Inference And Robustness Against Complex Backgrounds, Water Turbidity, And Varying Illumination. Subsequently, A Euclidean Distance Tracker Associates Detections Across Consecutive Frames, Enabling Reliable Multi-fish Tracking Without The Computational Complexity Of Deep Tracking Models. The Proposed System Ensures Real-time Performance With High Detection Accuracy And Low Identity-switch Rates, Making It Suitable For Practical Deployment In Aquaculture Farms. The Approach Can Support Applications Such As Fish Counting, Growth Monitoring, And Behavioral Analysis, Contributing To Sustainable And Intelligent Aquaculture Management.