In a modern energy network, there is a need to ensure that power distribution systems are stable and reliable in view of the increasing integration of renewable energies and variable load demands. Power quality (PQ) monitoring as well as fault detection makes it possible to prevent many disruptions such as voltage sag, swell, harmonics, and transients that may affect the equipment and operations. This chapter considers advanced methods for PQ monitoring with the help of sophisticated sensors, intelligent electronic devices (IEDs), and data-driven analytics in addition to conventional techniques such as Fourier Transform and Wavelet Transform for signal analysis. It investigates the accuracy of fault detection techniques such as traveling wave analysis, impedance-based approaches, and machine learning models in the detection and isolation of faults. It discusses issues that include real-time data processing constraints, high implementation costs, and insecurity. Emphasis on the future of PQ monitoring and fault detection has been given through discussions of emerging developments such as AI-driven diagnostics, self-healing grids, and IoT-enabled solutions. The results especially show how necessary it is to utilize creative strategies in improving grid resilience and ensuring continuous electricity delivery in increasingly intricate networks.