This paper presents novel models of Soft Computing (SC) Maximum Power Point Tracking (MPPT) controllers for Photo-Voltaic Water Pumping Systems (PVWPS) Based on DC Motor. Also paper discusses and evaluates the performance of the proposed models. The proposed methodology is mainly based on duty cycle prediction of the cuk converter to track the maximum power point (MPP) of the PV array. Indeed of the maximum water flow rate of PVWPS at varying climate conditions must ensure that the motor-pump set operates at MPP. The analytical modeling for the proposed MPPT controller has been implemented to predict the optimal duty cycle of the converter for MPPT achievement within the different operating conditions of solar radiation, ambient temperature, and total static head. Also, different Soft Computing (SC) Algorithms have been implemented and evaluated based on Particle Swarm Optimization (PSO) algorithm, Mine Blast Algorithm (MBA), and β-hill climbing (β-HC) algorithm. A comparative study between SC Algorithms, analytical modeling, and experimental works are given. Also evaluations of SC Algorithms are done to verify SC Algorithms' accuracy. This evaluation study has proven the significant improvement of daily water flow rate and also has specified the appropriateness of the SC algorithm for MPPT for a PVWPS.