<p>With the increasing demand for fresh water and the need for sustainable energy solutions, solar desalination technologies have emerged as a promising alternative. Traditional solar desalination systems often face challenges such as low evaporation rates, high-energy consumption, and slow desalination processes. This study aims to optimize solar desalination systems using vacuum-assisted heat exchangers. By applying a vacuum, the boiling point of water is reduced, which increases evaporation rates and accelerates the desalination process. The experimental setup involves circulating saline water through the vacuum chamber, where solar energy heats the water, promoting evaporation at lower temperatures. The evaporated water vapor is captured by a condenser unit and converted back to freshwater. The system’s performance is evaluated under various conditions to identify optimal parameters. Data analysis, employing deep learning techniques such as convolutional neural networks (CNNs) combined with long short-term memory (LSTM) models, aims to refine operating conditions for maximum efficiency. Multivariate adaptive regression splines (MARS) will be integrated to model the nonlinear and complex relationships between these variables in a more interpretable and flexible way. The findings indicate that vacuum-assisted heat exchangers at 50&#xa0;Torr significantly enhance evaporation rates to 3.15&#xa0;kg&#xa0;m⁻²&#xa0;h⁻¹ compared to traditional systems, achieving improved desalination performance. The study demonstrates the potential for substantial energy and cost savings while increasing sustainability in water purification processes. Future research will focus on scaling up the technology, improving material durability, and integrating advanced control systems for real-time optimization, offering potential solutions to global water scarcity issues.</p>