The Konkan Railway, excavation work completed in 1998 and serving India since then, faces climate-related challenges such as heavy monsoon rains, rockfalls, and slope failures that threaten railway operations in regard of train accidents and traffic interruptions. While the railway has implemented various geotechnical strengthening measures, concerns arise as climate change leads to more extreme weather events. Flattening slopes helps improve the stability of the railway track by reducing the risk of landslides, soil erosion, and boulder failures. A flatter slope contributes to safer and smoother train operations and is less prone to geological hazards. Railway infrastructure management involves numerous challenges, particularly in ensuring the stability and safety of slopes adjacent to the tracks. This research focuses on applying machine learning (ML) techniques in slope reconstruction works using controlled blasting to address these challenges. Blast-induced slope failure or rockfall during blasting operations are critical concerns that may interrupt normal traffic operations. They need accurate prediction for effective risk mitigation and rescheduling of traffic operations. Initially, at three locations of Konkan Railways, ML models have been developed using 490 datasets with the most inputs out of thirteen parameters using multicollinearity and Logistic Regression techniques based on minimum Akaike Information Criterion (AIC) values. This paper narrates the use of the best-trained Random Forest model for the rapid assessment of such failure during day-to-day blasts at two slope reconstruction sites of the Konkan Railway.