Photovoltaic (PV) systems are being implemented to provide sustainable energy, which, however are influenced by dust accumulation, shading, occurrence of hot spots, aging, cracks and temperature changes. Existing research has primarily focused on tasks like fault detection, degradation analysis, power prediction, Explainable Artificial Intelligence (XAI) and Federated Learning (FL), but not on the integration of predictive maintenance, adaptive decision making and power optimization. The proposed research aims to develop an integrated framework that is self-optimizing and intelligent for PV monitoring, degradation management and enhancement of PV power generation, referred to as Self-Optimizing Intelligent Adaptive Solar Control (SIASC). SIASC combines with a Convolutional Neural Network (CNN) for degradation and fault detection, Long Short-Term Memory (LSTM) for power reduction and degradation prediction, a Neuro-Fuzzy Inference System (NFIS) for assessing severity, Reinforcement Learning (RL) for adaptive corrective decision making and Adaptive Maximum Power Point Tracking (Adaptive MPPT) for operating-point optimization. The framework continuously monitors PV and environmental conditions, estimates degradation severity, identifies corrective actions and adapts power extraction by optimizing corrective actions through adaptive control. Simulation experiments using 10,000 PV samples achieved an accuracy of 99.36%, demonstrating effective and reliable PV fault management. Statistical analysis further supports the significance and consistency of the obtained results. Overall, SIASC provides an integrated approach for reducing degradation-related losses, improving energy generation and enabling intelligent PV energy management.