This study presents the modeling, simulation, and experimental validation of a fuzzy-logic-based intelligent control system applied to a cabinet-type solar dryer for drying Plantago major leaves under real climatic conditions in Tashkent (Uzbekistan) during the summer season. Traditional on/off and PID controllers often fail to maintain optimal drying conditions due to nonlinearities and fluctuations in solar radiation and ambient temperature. To address these limitations, a fuzzy inference system (FIS) was developed in MATLAB/Simulink using two input variables—internal air temperature and relative humidity—and one output variable—fan speed. The fuzzy system employed seven linguistic rules with triangular membership functions, allowing for smooth and adaptive real-time control. The experimental setup included a cabinet solar dryer loaded with 1,5 kg of Plantago leaves. During field trials, the solar irradiance ranged from 650 to 900 W/m² and ambient temperatures from 32 °C to 42 °C. The fuzzy controller’s performance was benchmarked against a conventional PID controller. The results showed that the fuzzy system reduced the total drying time by 22%, improved energy efficiency by 18%, and ensured a better moisture uniformity (±4%) across trays. Moreover, the post-drying phytochemical analysis confirmed better preservation of heat-sensitive bioactive compounds. The fuzzy controller maintained the drying air temperature within the optimal 45–50 °C range, even under fluctuating external conditions. This study demonstrates that fuzzy logic provides an effective solution for adaptive solar drying in hot continental climates. Future work will focus on integrating IoT-based remote monitoring and hybrid control optimization to enhance automation and scalability.