The increasing global reliance on renewable energy necessitates efficient and reliable solar power systems. However, challenges such as unexpected failures, performance degradation, and infrastructure instability hinder large-scale adoption. This research proposes an AI- driven predictive maintenance framework integrated with cloud-based optimization to enhance solar energy efficiency and fault resilience. By leveraging deep learning (DL) models, including Artificial Neural Net- works (ANNs), Convolutional Neural Networks (CNNs), Long Short- Term Memory (LSTM), and a Hybrid CNN-LSTM architecture, the system accurately detects faults, predicts degradation trends, and optimizes energy output in real-time. Experimental results demonstrate that the Hybrid CNN-LSTM model achieves an F1- score of 97.3%, significantly outperforming conventional maintenance approaches. Additionally, cloud-based AI optimization increases energy output by 12.1%, reinforcing its effectiveness in reducing operational inefficiencies. While the system demands higher computational resources, its real-time inference capabilities minimize downtime and enhance grid stability. Future research should focus on lightweight AI models, decentralized processing, and enhanced cybersecurity measures to further improve scalability and efficiency. This study highlights the potential of AI-driven predictive maintenance as a transformative approach for ensuring the sustainability and reliability of next- generation solar power systems.