With the growing demand for clean and sustainable electricity, modern power distribution systems must ensure not only efficiency but also high-power quality. This study proposes and investigates an advanced three-phase three-wire Inductively Coupled Distributed Static Compensator integrated with a Deep Belief Learning Network control strategy for enhancing current-based power-line conditioning. The proposed system incorporates an Inductive Filtering Transformer alongside a conventional Directly Coupled DSTATCOM, acting as an impedance-matching interface among the grid, compensator, and load. A mathematically grounded DBLN framework is developed to generate optimized switching signals for both DC- and IC-DSTATCOM configurations, ensuring dynamic adaptability and precise control. Extensive simulation studies in MATLAB/Simulink validate the effectiveness of the system under various static and dynamic loading scenarios. Key performance improvements include significant reduction in Total Harmonic Distortion, improved power factor, effective voltage regulation, and reduced compensator size. The system’s performance is further validated through experimental implementation, demonstrating compliance with international standards such as IEEE 519-2017 and IEC 61000-3-12. These findings underscore the potential of the DBLN-based IC-DSTATCOM as a robust and scalable solution for smart grid applications.