This study focuses on improving power electronic components and devices, which have gained popularity due to their compact size and precise control over the output voltage and current. They are widely used in renewable energy systems, such as converters and inverters, and in industrial drives as control devices. However, the switching process in power electronic components introduces nonlinear behavior, leading to the generation of harmonics and power quality problems. To address these issues, various methods and techniques recommended by industry standards have been proposed to mitigate or eliminate harmonics and ensure the power quality. These standards provide limits and guidelines for assisting customers and manufacturers in maintaining acceptable power quality levels. Machine-learning algorithms offer a promising approach for improving power quality by leveraging data from a system to make predictions or classifications. In the context of power electronics, machine-learning algorithms can be trained to classify fault types, identify the exact location of faults, predict the remaining life of power electronic components, and detect voltage disturbances. Different machine learning techniques can be employed, depending on the specific application. For example, fault classification and fault location identification can be achieved through supervised learning algorithms, whereas predicting component life and detecting voltage disturbances can utilize techniques such as regression or anomaly detection. By leveraging the power of machine learning, enhanced reliability, performance, and efficiency can be achieved in various applications, thereby contributing to the overall advancement of power electronics technology.