Globally, power quality issues incur substantial costs. In the United States, power quality problems contribute to a $150 billion annual cost, covering lost productivity, equipment damage, and safety hazards. Smart intelligence-based methods can potentially cut these costs by up to 50%. In India, power quality disturbances result in a $10 billion annual cost involving equipment damage, productivity losses, and customer dissatisfaction. The adoption of smart intelligence-based power quality methods in India is projected to grow annually by 25% for the next 5 years due to increasing grid demands. In today’s intricate power landscape, dependable electrical systems are crucial. Power quality disturbances, including voltage variations, harmonics, and flicker, can disrupt sensitive equipment, resulting in financial losses and safety risks. Addressing these challenges, smart intelligence-based methods emerge as promising solutions. This chapter systematically explores the application of artificial intelligence, machine learning, and data analytics for elevated power quality monitoring, assessment, and regulation. Such intelligent approaches optimise power system performance, reduce downtimes, and ensure a consistent supply of high-quality electrical energy. The assimilation of smart intelligence-based methods emerges as a promising avenue to address these challenges effectively. Harnessing the capabilities of these intelligent paradigms empower power systems to attain optimal performance, curtail downtimes, and ensure a steadfast provision of high-grade electrical energy.