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Since solar energy is intermittent, finding the best solutions is a challenge. This paper provides a thorough review of solar energy systems' generic optimization objectives. The intelligent optimization techniques for solar energy systems are discussed, including their functions, constraints, contributions, mathematical models, and analysis methods. Optimization studies using new and traditional generation techniques are analyzed, and a few optimization methods, including combined hybrid algorithms, are presented. New generation artificial intelligence algorithms have been most widely used during the last decade, needing less computational time. They have good convergence and better accuracy than traditional optimization methods. They can scan local and global optima and do robust calculations. Solar system optimization has demonstrated remarkable benefits in size, load demand, and electricity output. The improvements reduce operating expenditures, power losses, and peak output integration and controllability. With a 50% rise in power prices, the optimal number of solar collectors rises by approximately 25%. However, with adjustment as per optimization techniques, the solar absorption cooling system's maximum thermal efficiency can be increased up to 75%.The present study recommends using two or more algorithms to overcome the curbs of a single algorithm. The main aim of optimization strategies, according to this assessment, is to reduce capital expenditures, operation and maintenance expenses, and emissions while improving system reliability. The paper also briefly describes several solar energy optimization challenges and issues. Lastly, some practical future approaches for developing a reliable and efficient solar power system are proposed for developing the complex renewable energy-based hybrid system.