With the continuous growth of rail track geometry irregularities due to aging, environmental factors, and wheel loads, rail track requires frequent maintenance. Railroads often rely on the precise and correct localization and identification of track irregularities that significantly destroy infrastructure and create life-threatening environments. Therefore, monitoring the conditions of the railroad tracks is vitally essential for ensuring safety, reliability, and cost-efficiency of operations. Consequently, agencies inspect all tracks twice a week per federal track safety regulations. However, their existing methods of track inspection are expensive, slow, require track closure, and pose a high risk to workers. The technical constraints of these methods impede network-wide scaling to all railroads. More frequent, continuous, and network-wide monitoring to detect and fix irregularities can help to reduce the risk of harm, fatalities, property damages, and possible financial losses. This work introduces and develops a generalized, scalable, affordable inspection and monitoring system called Railway Autonomous Inspection Localization System (RAILS). In particular, the study aims to detect, locate, and characterize track-related issues. The research focuses on designing RAILS architecture, implementing data collection, and building algorithms that include inertial signal feature extraction, data processing, signal alignment, and signal filtering. Case studies validate and characterize system accuracy by estimating the position of detected irregularities based on a linear referencing system. In one case study, the estimated position of the irregularity is compared with the actual position of ground truth data (GTA) observed by a railroad inspector. In another case study, a railroad inspector verifies the estimated position of the irregularity to demonstrate the system’s effectiveness and affordability for practical applications. Therefore, railroad agencies employing the developed methods will benefit from reliable track and equipment conditions to make informed decisions that will lead to resource optimization. The conclusion of this research outlines the significant potential of the proposed system, including limitations and future work for practical, real-time, and autonomous implementation.