Aik Nai Chiek0000-0003-4739-5567Tan Kok Eng0000-0003-0885-8100Raymond Ling Leh Bin2026-10-152026-10-1520269783032168825978303216883210.1007/978-3-032-16883-2_4https://dspace-cris.utar.edu.my/handle/123456789/11759Predicting financial distress is crucial for firms, investors, and policymakers to manage risks and maintain financial stability. This study applies machine learning to predict financial distress among Malaysian non-financial firms using data from 2015 to 2023. Among the models tested, Random Forest proves the most reliable, consistently outperforming other classifiers across training, validation, and testing. With optimised hyperparameters, the model ensures high accuracy and generalisability, detecting distress patterns with minimal errors. This reinforces the significance of machine learning in early detection and risk assessment while validating Random Forest’s reliability in predicting financial distress. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.en-USEarly detectionFinancial distress predictionMachine learningRandom ForestRisk managementForecastingInvestmentsLearning systemsRandom forestsRisk assessmentFinancial distressFinancial stabilityMachine learning modelsMachine-learningMalaysiansPolicy makersRisks managementtext::conference output::conference proceedings::conference paper