Maximizing the efficiency of photovoltaic (PV) energy harvesting systems is essential for improving the sustainability and cost-effectiveness of solar power. This paper presents a novel hybrid MPPT controller that combines fuzzy logic and proportional-integral (PI) control optimized using particle swarm optimization (PSO) for enhanced performance in standalone photovoltaic (PV) systems. The proposed controller is designed to address the limitations of conventional MPPT methods, particularly in complex and dynamic environments such as those encountered in arid regions. The novelty of this approach lies in the synergy between the fuzzy-PI controller and PSO. The fuzzy-PI controller provides a rapid and adaptive response to changes in environmental conditions, leveraging its rule-based system to handle nonlinearities in the PV power curve. PSO, a nature-inspired optimization algorithm, is employed to fine-tune the parameters of the hybrid fuzzy-PI controller, ensuring optimal performance across a wide range of operating conditions. This combination allows the hybrid controller to track the global maximum power point accurately even under challenging conditions like partial shading, where traditional algorithms may falter. A comparative study is performed with a hybrid P&O-PI MPPT controller optimized by PSO and the conventional perturb and observe (P&O) techniques. Comprehensive simulation studies and real-world testing demonstrate that the hybrid fuzzy-PI MPPT controller optimized by PSO demonstrates its best performance on the plan of the speed tracking global MPP (0.30 s), accuracy, reduced oscillations, and higher overall energy efficiency (99.70%) compared to conventional MPPT methods. The results highlight the potential of this approach to enhance the reliability and effectiveness of standalone PV systems, particularly in harsh environments, making it a promising solution for maximizing solar energy utilization.