This paper proposes an effective AI control technique‐based improved QZS‐CMI topology for interfacing PV system. Generally, the interface between the PV dc source and the load is accomplished by a QZSI. In the paper, the proposed control scheme is a consolidated execution of both the RF and CSA named as RFCSA. The principle goal of the proposed approach is to determine the efficiency of the PV system by the maximum power extraction. Here, the modeling design of QZS‐CMI is enhanced to deliver the maximum power from a PV power generating system. Initially, the objective function is defined based on their controller parameters and constraints such as voltages, current, power, and modulation index etc. These parameters are applied to the inputs of the proposed RFCSA technique. The proposed RFCSA technique is improved the voltage profile, power delivery, and minimizing the power oscillations while sharing the power to the load. The maximum power delivery to the load is ensured by an AI technique based on MPPT. The proposed AI‐based improved QZS‐CMI regulated the shoot‐through duty ratio and reduced the modulation burden. Moreover, the proposed RFCSA control technique reduced the injected power and regulates the dc link voltage, current, and frequency conditions. The proposed technique is implemented in MATLAB/Simulink platform, and their output performance is compared with the existing methods for different loading conditions.