The integration of solar-powered cooling systems with conventional fossil fuels has emerged as a promising solution to meet the growing demand for sustainable and energy-efficient cooling technologies. This paper presents a novel methodology for optimizing hybrid refrigeration systems powered by solar energy and fossil fuels, aimed at advancing smart living paradigms while mitigating environmental impact and reducing reliance on non-renewable resources. The proposed approach combines thermoenergetic analysis, artificial intelligence modeling, and metaheuristic optimization techniques to systematically analyze, model, and optimize hybrid refrigeration systems. Specifically, the study conducts a comprehensive thermoenergetic analysis of the hybrid GAX cycle, develops surrogate models using Artificial Neural Networks (ANN), and performs optimization and feasibility analyses leveraging Particle Swarm Optimization (PSO) algorithm. The integration of these methodologies enables the identification of optimal system configurations and control strategies that enhance energy efficiency, economic viability, and environmental sustainability. Through a case study, the effectiveness of the proposed methodology is demonstrated, yielding promising results in terms of maximizing thermal energy generation, minimizing carbon emissions, and optimizing economic performance. The findings contribute to the development of innovative solutions for sustainable cooling technologies, thereby advancing the frontier of smart living and fostering a more resilient and environmentally conscious future.