In this research, the Federal Railroad Administration (FRA) sponsored a team from the University of Delaware to develop a multidimensional, time-based Track Safety and Quality Index (TSQI) for assessing and monitoring the condition of railway tracks. The index captures the temporal dependencies and uncertainties associated with track parameters by incorporating machine learning techniques and a multivariable normal distribution. The developed 3D track quality index (TQI) incorporates probabilities of exceeding predefined safety limits for the mid-chord offset of the track geometry data set by FRA. A comprehensive assessment of the track’s condition is obtained by calculating the probability of exceedance for each measured parameter based on the multivariable normal distribution. The index provides an overall measure of the likelihood of the track quality exceeding specified thresholds for multiple parameters simultaneously. The 3D TQI is a valuable tool for assessing and monitoring the condition of railway tracks. It integrates machine learning techniques, probability analysis, and customization options to offer a comprehensive and customizable approach to track evaluation. The index enhances safety, optimizes maintenance practices, and contributes to the reliable operation of railway infrastructure.