This research explores the role of artificial intelligence (AI) in enhancing the efficiency and reliability of solar thermal, photovoltaic (PV), and hybrid energy systems. As the transition from fossil fuels becomes increasingly crucial due to their contribution to global warming and resource depletion, optimising solar energy systems through AI-driven technologies has become imperative. The study examines solar thermal and PV applications for their ability to generate electricity, heat buildings, and support industrial processes, demonstrating how AI techniques, such as artificial neural networks and machine learning models, enhance system performance and enable real-time monitoring. Additionally, hybrid energy systems, which integrate renewable and non-renewable sources like wind, solar, diesel, and fuel cells, are extensively analysed. AI applications, including support vector machines and genetic algorithms, play a key role in improving the efficiency of these systems by forecasting energy production, optimising storage, and minimising system losses. The research concludes with a SWOT analysis, identifying the strengths, weaknesses, opportunities, and threats of AI integration in energy systems while providing strategic recommendations for future research, policy development, and technological innovation. By leveraging AI in solar and hybrid energy solutions, this study offers a comprehensive framework for enhancing sustainability, reducing emissions, and ensuring a stable energy supply.