Photovoltaic systems are gaining traction for the generation of sustainable energy sources. But the functioning faces the influence of various types of degradations, which include dust pollution, shadow effect, hotspot formation, aging effects, crack detection, rise in temperature, and many more. Some studies have been done to explore individual techniques for faults detection, degradation detection, power prediction, Explainable Artificial Intelligence, and Federated Learning. Despite all these efforts made by researchers, there have not been any systems that would provide an all-round solution for degradation detection, predictive maintenance, adaptive decision making, and optimization of power generated. This research proposed an innovative system called Self-Optimizing Intelligent Adaptive Solar Control (SIASC), which will be designed for intelligent Photovoltaic (PV) control and enhancement. SIASC will comprise Convolutional Neural Network (CNN) for detecting degradation, Long Short-Term Memory networks for the prediction of reduction in power, neuro-fuzzy inference system to determine the severity level, reinforcement learning for making adaptive decisions, and Adaptive Maximum Power Point Tracking algorithm for optimizing the extracted power. The obtained results from the simulation experiments using 10,000 PV units confirm the high efficiency achieved through the SIASC framework. With the SIASC framework, 99.36% Accuracy, 99.08% Precision, and 98.99% F1-Score are achieved. All the statistical calculations also reveal the efficiency and significance. It can be concluded from the results that SIASC is highly efficient and can minimize losses due to degradation, maximize energy generation, guarantee reliability, and provide an intelligent solution for future photovoltaic energy management systems.