The consequences of global warming include changes in rainfall patterns and increase in flood risks or droughts. The frequent unexpected climate change phenomena, especially the severe flood occurrences, necessitate improvements in the related weather monitoring instruments and techniques. The rain measuring system, whether the conventional rain gauges or the more advanced Remote Sensing and Transmission Unit (RSTU) panel, can only be sparsely installed at suitable location, hence they are considered as point rain measurement. The paper introduces an innovation to point rain estimation, named GMS-Rain, which estimates convective rainfall using information from the Geostationary Meteorological Satellite-5 (GMS-5) infrared (IR) images and numerical weather prediction (NWP) products in an Artificial Neural Network model. Although the estimates are indirect, meteorological satellites with fine temporal and spatial resolution cover broader areas that may be inaccessible or that may cause difficulties with the traditional rainfall measurement such as the deep forests, large water bodies or rigid mountains, therefore should be taken as complementary to rain gauge or radar measurements. In addition, the rain estimation from the observation of cloud development would enable earlier forecast of critical storm events. The GMS-Rain model is also a potential input to a hydro-meteorological flood forecasting system with an improved lead time of warning. Rainfall estimates from the GMSRain are validated against previously recorded hourly and total accumulated Thiessen arealaveraged gauged rainfall values with coefficient correlation values of 0.63 for 0.91 respectively, while an extra lead time of around 2 hours is gained when the model is coupled with a rainfallrunoff model to forecast a flood event in the upper Klang River Basin.