Solar water pumping systems are a crucial application of renewable energy, especially in rural areas where traditional electricity infrastructure may be limited or nonexistent. These systems utilize solar energy to drive water pumps, offering a sustainable and economical solution for water provision. Remote controllers further enhance the convenience and efficiency of solar water pumping systems by enabling remote monitoring and control. This article introduces a solar water pumping system that incorporates an optimized Fractional-Order Proportional-Integral-Derivative (FOPID) controller. By fine-tuning the FOPID parameters, the system can achieve superior performance and reliability, making it well-suited for operation under diverse environmental conditions. The photovoltaic (PV) panel data is transmitted to a remote controller via the Internet of Things (IoT). The remote controller employs the Adaptive Weighted Dwarf Mongoose Optimization Algorithm (ADMOA) to optimize the and parameters of the FOPID controller of solar PV panel. These optimized parameters are then transmitted to the FOPID controller to ensure optimal operation of the solar water pumping system. To evaluate the effectiveness of the ADMOA method, it was compared to traditional trial-and-error tuning methods based on output power, stator current, rotor speed dynamics, and torque. Thus, the simulated findings consistently reveal the superiority of the ADMOA algorithm in terms of convergence analysis and solution quality compared to other reported techniques.