Partial shading poses a significant challenge in photovoltaic systems by creating multiple peaks in the power-voltage curve, complicating the task of accurately tracking the Maximum Power Point. Traditional maximum power point tracking methods often struggle to identify the true Global Maximum Power Point, leading to suboptimal energy harvesting. This paper proposes a novel hybrid tracking framework that integrates fuzzy logic, synthetic data generation using Generative Adversarial Networks (GANs), and time-series modeling with Transformer architectures. Fuzzy logic improves resilience to input uncertainties by translating raw data into interpretable fuzzy values. GANs augment the dataset by generating realistic synthetic samples, thereby improving generalization. The Transformer model leverages self-attention mechanisms to capture long-term temporal patterns in solar irradiance and power profiles. By combining these strengths, the proposed method delivers a robust and accurate global maximum power point tracking solution, particularly under dynamic and partially shaded environments. Experimental results demonstrate its superior performance and scalability compared to conventional maximum power point tracking approaches.