The Si insulated-gate bipolar transistor (IGBT) and SiC MOSFET hybrid switch offers an effective approach to achieve both high efficiency and cost-effectiveness. Given the varying operational conditions of VVVF (Variable Voltage Variable Frequency) inverters, adaptive control of delay-time and frequency division is crucial to optimizing performance. However, traditional optimization methods are computationally expensive which requires too long time to complete optimization, making them impractical for use in VVVF inverters in vehicles such as trains or cars. This paper introduces a novel approach that divides the optimization process into offline and online stages, employing a Deep Learning approach utilizing Neural Net (NN) algorithm in both stages. This method significantly reduces the time required for optimization compared to conventional adaptive techniques, enabling real-time applicability in VVVF inverters for vehicles. The proposed method is validated using simulation data to train the NN model and test its accuracy, which can be further evaluated with real-world data in future studies.