Accurate and efficient solar tracking is critical for maximizing photovoltaic (PV) system performance, yet conventional tracking methods often struggle with environmental variability, sensor noise, and scalability limitations. This paper proposes a novel intelligent adaptive control framework for solar trackers, leveraging advanced machine learning, model predictive, and hybrid fuzzy logic algorithms to optimize panel orientation in real time. The proposed system overcomes the key drawbacks highlighted in recent literature by enabling rapid adaptation to changing weather conditions, robust performance under partial shading, and resilience against sensor inaccuracies. Comprehensive fault detection, automatic calibration, and predictive maintenance functionalities are integrated to minimize operational costs and system downtime. Designed for compatibility with bifacial and next-generation PV technologies, the framework supports seamless scaling from residential to utility-scale deployments and facilitates integration with IoT-based monitoring and smart grid platforms. Extensive validation using both simulation and real-world testbeds demonstrates significant improvements in energy yield, reliability, and cost-effectiveness compared to state-of-the-art tracking systems. These advancements position the proposed intelligent adaptive control algorithms as a comprehensive and sustainable solution for enhancing solar tracker efficiency in diverse operational contexts.