This study clarifies the feasibility of implementing a machine learning (ML)-oriented cascaded inductively coupled distributed static compensator for power quality (PQ) enhancement. The issues involved in declining the power quality (PQ) using a cascaded direct coupled static compensator (CDC-DSTATCOM) were identified as a hazardous disappointment. Hence, to improve the quality, the coupling transformer is served in unification with CDC-DSTATCOM. Also, the recent growth of machine learning (ML) systems and the progression of computational resources, with unpredicted data obtainability, have inspired the researchers. In this study, density-based spatial clustering of application with noise (DBSCAN) is employed by using its own learning mechanism (LM) using MATLAB/Simulink. This controller is composed of six subnets, with six being used for active tuned weight extraction, while only three are allocated to the reactive part. Among them, six subnets are employed for active tuned weight extraction whereas other three subnets are used for reactive part. Moreover, the above-said devices are triggered with the help of generated reference supply current. To illustrate how CDC-DSTATCOM and CIC-DSTATCOM work, we take a close look at a real-world case study. In conclusion, the CIC-DSTATCOM is enhanced in a more healthful way than others in terms of reducing harmonics, improving power factor, balancing loads, regulating potential, etc.