Artificial intelligence: a ray of hope for solar still desalination reinforcement—a review

Each sector strives to reach optimization using artificial intelligence. Harnessing AI has more accuracy and nearly null error. Nowadays, AI plays a broader role in data analysis, automation, machine learning, and optimization. The scope of the review is to have a more specific focus on the Artificial Neural Network (ANN) application in the solar stills. Sustainable potable water production from solar still desalination is an emerging trend. Various strategies continuously upcycle thermal efficiency and water productivity. The effect of each meteorological and solar still criterion on clean water productivity is complex. Therefore, this study explored current water stress, the necessity of solar desalination, the importance and integration of ANN in solar still desalination potential, and the impact of their results on efficiency improvement. Highlights of the available ANN features and their topology, along with driven algorithms, are scrutinized concerning solar still desalination. The application outlook of ANN in water treatment is weighed up. Different activation functions and evaluation criteria used in ANN-enabled solar desalination are discussed. Specifically highlight the more relevant studies on the ANN model’s potential to enhance solar still desalination productivity, thermal efficiency, weather forecasting, and automation. Discuss the effective utilization of ANN under solar desalination prospects with challenges. This review concluded that the rapid impact of ANN on solar still desalination produces a new version of the solar still desalination system and highlights ANN as one of the sprouts for attaining sustainable, effective control of clean water and sanitation in a highly volatile environment.