This work presents a Distributed Static Compensator (DSTATCOM) intended for low-voltage distribution systems, controlled through a Zordan Least Mean Square (ZLMS) optimization framework. Practical deployment of adaptive controllers in power quality conditioners is often constrained by slow convergence, sensitivity to parameter variation, difficulty in handling unbalanced loads, and increased computational effort during real-time implementation. To overcome these limitations, the proposed control approach incorporates parallel processing to enable effective shunt compensation while satisfying IEEE-519 harmonic requirements. An enhanced form of the Adaptive Least Mean Square (ALMS) method is also developed, and its behaviour is evaluated under both balanced and disturbed operating conditions. The neural structure consists of six subnetworks dedicated to extracting active and reactive current components separately, enabling improved estimation reliability. The ZLMS adaptation mechanism refines weight adjustment using learning rate control, normalization factors, and historical weight information, leading to faster convergence and reduced steady-state error. Validation is performed through MATLAB/Simulink simulation and experimental testing using a dSPACE-1108 real-time platform. The results demonstrate notable improvements in power factor, harmonic mitigation, voltage regulation, and load balancing during dynamic operating conditions when compared with conventional ALMS-based control.