<p>This paper presents an innovative approach to improving the competence of space vector modulation (SVM) approaches in parallel multicellular converters with monolithic inter-cell transformers (PMC-ICTs) through the application of artificial neural networks (ANNs). Traditional SVM approaches often face challenges in managing nonlinearities and maintaining stability under varying operational conditions. In electrical machine control applications, using traditional SVM approaches gives a high value of total harmonic distortion (THD) of current which is undesirable. By integrating ANNs with SVM, the control accuracy is enhanced, the THD value is reduced, and robust and adaptable control is achieved. Therefore, the SVM-ANN strategy is a modified strategy for the SVM strategy, as it is characterized by simplicity and ease of implementation with high robustness. The proposed intelligent SVM strategy utilizes the learning capabilities of ANNs to optimize switching schemes, leading to significant improvements in output power quality and system stability. This proposed strategy has been implemented in the MATLAB environment. Simulation results demonstrate a marked reduction in THD by approximately 30.3%, from 40.77% to 10.47%, and a 73% reduction in voltage ripples compared to conventional methods. These improvements highlight the efficacy of ANN-based control in advancing the performance of PMC-ICT systems. This research provides a comprehensive analysis and comparison of traditional and ANN-enhanced techniques, offering valuable insights for future developments in intelligent control systems for power converters. The results obtained make the proposed strategy and PMC-ICTs a promising solution in future for their use in energy systems based on wind turbines, where the rotating current of the generators can reach very high values.</p>