The directly coupled distributed static compensator (DC-DSTATCOM) is often utilized to achieve better power quality (PQ) in the power distribution network (PDN). However, this compensator faced challenges like poor adaptability performance and more maintenance costs due to the integration of several types of energy resources. To overcome the above-said limitations, the inductively coupled distributed static compensator (IC-DSTATCOM) using Deep Belief Learning Network (DBLN) technique is proposed. The power transfer capability of the IC-DSTATCOM is examined by considering the impedance matching principle of the distributed static compensator (DSTATCOM), source and load. Besides this, the dependent parameters are combined with the convergence factor and learning rate to achieve the approximate tuned weight by using the suggested learning mechanism. The generalized mathematical equations are illustrated using MATLAB/Simulink to generate the switching pulses. The simulation studies of both DC-DSTATCOM & IC-DSTATCOM are performed to evaluate the transient behaviour and robustness under different states of loading. The proposed system is augmented with a superior performance in terms of harmonics curtailment, improvement in power factor (p.f), load balancing, potential regulation etc. The international standard regulatory guidelines IEEE-519–2017 and IEC- 61,000–1 are imposed to evaluate the effectiveness of the simulation and d-SPACE-based experimental study.