<p>Electrical Submersible Pumps (ESPs) are a critical component of artificial lift systems in the oil and gas industry, valued for their efficiency in high-volume production and adaptability to a wide range of reservoir conditions. However, their performance is often compromised by frequent failures arising from mechanical degradation, electrical faults, operational stresses, and harsh environmental factors. This review provides a comprehensive and systematic evaluation of ESP failure mechanisms and their root causes, with particular emphasis on diagnostic methodologies and predictive maintenance strategies. Recent advancements in machine learning such as XGBoost, Long Short-Term Memory (LSTM) networks, and Principal Component Analysis (PCA) are explored for their applicability in early fault detection and condition-based monitoring. The review also presents best practices in ESP installation, material selection, and real-time surveillance to enhance system reliability. Field-based case studies are included to illustrate the practical implementation of Root Cause Analysis (RCA) and predictive analytics, demonstrating substantial reductions in failure rates, extended pump run lives, and significant cost savings. The findings underscore the necessity of integrating advanced diagnostics and intelligent maintenance frameworks to ensure sustained ESP performance in increasingly complex and demanding production environments.</p>