Reliable prediction of rainfall extremes is vital for disaster management, particularly in the context of increasing rainfall extremes due to global climate change. Physical-empirical models have been developed in this study using three widely used Machine Learning (ML) methods namely, Support Vector Machines (SVM), Random Forests (RF), Bayesian Artificial Neural Networks (BANN) for the prediction of rainfall and rainfall related extremes during Northeast Monsoon (NEM) in Peninsular Malaysia from synoptic predictors. The gridded daily rainfall data of Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) was used to estimate four rainfall indices namely, rainfall amount, average rainfall intensity, days having >95-th percentile rainfall, and total number of dry days in Peninsular Malaysia during NEM for the period 1951–2015. The National Centers for Environmental Prediction (NCEP) reanalysis sea level pressure (SLP) data was used for the prediction of rainfall indices with different lead periods. The recursive feature elimination (RFE) method was used to select the SLP at different NCEP grid points which were found significantly correlated with NEM rainfall indices. The results showed superior performance of BANN among the ML models with normalised root mean square error of 0.04–0.14, Nash-Sutcliff Efficiency of 0.98–1.0, and modified agreement index of 0.97–0.99 and Kling-Gupta efficient index 0.65–0.96 for one-month lead period prediction. The 95% confidence interval (CI) band for BANN was found narrower than the other ML models. Almost all the forecasted values by BANN were also found with 95% CI, and therefore, the p-factor and the r-factor for BANN in predicting rainfall indices were found in the range of 0.95–1.0 and 0.25–0.49 respectively. Application of BANN in prediction of rainfall indices with higher lead time was also found excellent. The synoptic pattern revealed that SLP over the north of South China Sea is the major driver of NEM rainfall and rainfall extremes in Peninsular Malaysia. •Physical-empirical models are developed using three machine learning methods for prediction of rainfall extremes.•Four rainfall indices in Peninsular Malaysia during northeast monsoon are forecasted with uncertainty levels.•The sea level pressure (SLP) data is used as predictors for the development of models.•The recursive feature elimination method is used for the selection of predictors to consider non-linearity in relationship.