The presented study underscores the importance of maintaining the structural stability and operational efficiency of critical infrastructure, especially dams, to ensure its long-term viability. By integrating advanced methods such as YOLOv4-CSP for automated dam detection and Long Short-Term Memory (LSTM) models for stress and erosion prediction, the study offers a more effective and accurate approach compared to conventional techniques like Multiple Linear Regression (MLR) and Support Vector Machines (SVM). The LSTM model is proficient at capturing temporal dependencies and nonlinear patterns, providing reliable predictions of dam behavior. The study anticipates a total erosion of 99 m2 over five years, leading to the proposal of a reinforcement plan with an estimated cost of ₹6,150,000. Historical data validation further supports the accuracy of the LSTM model, demonstrating the potential of machine learning to enhance predictive capabilities and enable proactive maintenance of essential infrastructure such as the Koyna Dam.