Solar drying systems provide an energy-efficient and environmentally sustainable solution for processing agricultural and medicinal products; however, their performance is strongly influenced by nonlinear process dynamics, fluctuating solar irradiation, and continuously changing ambient conditions. These uncertainties often lead to unstable temperature and humidity regimes, increased energy consumption, and the degradation of product quality when conventional fixed-parameter control strategies are applied. Therefore, the development of intelligent and adaptive control approaches capable of ensuring robust operation under highly variable conditions remains an important challenge in modern automation and mechatronic systems. In this work, an advanced adaptive control framework based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed for a cabinet-type solar drying system. The controller is formulated using a first-order Sugeno fuzzy inference structure with Gaussian membership functions and trained through a hybrid learning algorithm that combines least-squares estimation with gradient-based optimization. To further enhance adaptability and robustness, the ANFIS parameters are optimized using a particle swarm optimization (PSO) algorithm, while physical constraints derived from heat and mass transfer principles are explicitly incorporated into the control design. The complete control architecture is implemented in a MATLAB/Simulink environment, enabling digital-twin-based simulation and comprehensive performance evaluation. The simulation results demonstrate that the proposed optimization-enhanced adaptive ANFIS controller significantly improves temperature and humidity regulation compared to conventional control approaches. Faster setpoint tracking, reduced overshoot, and improved disturbance rejection are achieved under variable solar irradiance and ambient conditions. Quantitative analysis indicates a reduction in settling time of approximately 30% and a decrease in energy consumption by about 12-18% while maintaining stable and efficient drying regimes. The obtained results confirm that integrating adaptive neuro-fuzzy control, metaheuristic optimization, and physics-informed constraints provides an effective and scalable solution for complex nonlinear solar drying processes. The proposed framework can be extended to other renewable energy-driven thermal and mechatronic systems.