Electric rail systems are vital for sustainable urban transport but generate noise that impacts communities and passengers. Noise detection and mitigation in electric rail systems provides several key benefits, including improved quality of life for nearby residents by reducing disruptive noise pollution. It enhances passenger comfort through quieter cabins and smoother rides, leading to better travel experiences. For operators, mitigation measures like optimized wheel designs and track maintenance lower long-term maintenance costs by minimizing wear and tear from vibrations. Additionally, compliance with environmental regulations is achieved, avoiding legal penalties and fostering positive community relations. This paper examines key noise sources in electric rail operations, including wheel-rail interaction, aerodynamic effects, traction systems, and electromagnetic fields. Acoustic measurement techniques such as beamforming, near-field acoustic holography, and pass-by testing are evaluated for their effectiveness in identifying and quantifying noise emissions. Computational models, including finite element and statistical energy analysis, are explored for predicting noise propagation. Mitigation strategies focus on optimized wheel and rail design, active noise control, and passive damping materials. Emerging technologies like machine learning for noise prediction and metamaterials for sound absorption show promise for future applications. This study provides a comprehensive framework for addressing noise challenges in electric rail systems, contributing to quieter and more sustainable urban mobility.