This chapter provides an in-depth analysis of the key considerations and challenges involved in integrating ML into real-time power electronics and motor drive applications. It begins by addressing challenges such as limited data availability and the need for rapid tuning and control speeds, which are crucial in high-performance power electronic systems. These factors often complicate the deployment of ML models, necessitating solutions that deliver accuracy, speed, and efficiency. Following this, this chapter presents practical steps for deploying ML in real-time power electronics environments, with a focus on digital signal processors (DSPs). DSPs are widely implemented in real-time applications due to their optimized computational capabilities, making them particularly well-suited for handling the rapid processing and control demands of power electronics and motor drive systems. This chapter then presents strategies for integrating ML into DSPs, including both direct ML implementations and hybrid approaches that combine traditional control algorithms with ML components. Various effective neural network algorithms that have been successful applied in DSP applications are examined, along with insights into their specific implementation techniques. Finally, this chapter provides comprehensive guidelines and considerations for implementing ML on DSPs, covering stages from data collection to model deployment.