The most effective way to control the speed of the motor is to use the variable frequency drive (VFD) control. The Insulated Gate Bipolar Transistor (IGBT) is one of the most crucial power semiconductor devices in VFD in motor control systems and is also susceptible to failure. High junction temperatures and fluctuations in junction temperature are primary contributors to the aging and failure of IGBT modules. Precise monitoring of the junction temperature of the IGBT module is essential for reducing maintenance expenses and enhancing the reliability of the motor drive equipment. This paper introduces a time series estimation model based on a deep learning network to estimate the junction temperature of IGBT modules in motor control systems. By using historical operating data, the time series estimation model based on a deep learning network can capture the dynamic process in the system more accurately and estimate the junction temperature of the IGBT module better. The test results show that the proposed method is highly accurate for junction temperature estimation of IGBT modules in motor control systems.