Rail infrastructure is a critical component of the national economy, with track systems representing some of the most valuable assets in the railroad industry. Key datasets, including track layout data, condition monitoring data, repair and maintenance records, are frequently siloed across departments, limiting their combined utility in streamlining track asset management and safety analyses. This research addresses these challenges by developing an integrated informatics-enabled intelligent platform to unify disparate datasets into a centralized data warehouse. The platform leverages temporal-spatial data analysis to integrate and overlay track chart data with various types of condition monitoring data and other operational records, enabling advanced data analytics for track asset management. Statistical and machine learning techniques will be applied to analyze and predict track changes, identify degradation trends, and ultimately support proactive inspection and maintenance planning. The project will focus on building a digital intelligent platform in collaboration with NJ Transit that provides seamless access to their various types of enterprise and asset data, supporting complex data queries and condition monitoring data analysis. While developed in close collaboration with NJ Transit, the platform is adaptable for use by other railroads facing similar challenges. This research advances the state of infrastructure management by breaking down data silos and enabling data-driven decision-making. The outcomes will contribute to improved safety and operational reliability of rail systems.