Rainfall patterns exhibit a noticeable trend of variability, marked by significant fluctuations over time, where periods of heavy rainfall may be followed by years of relatively low precipitation. This variability has increased the demand for accurate rainfall forecasts. Thus, enhancing rainfall forecast accuracy in the face of climate variability is crucial. Achieving greater accuracy in rainfall forecasts requires utilizing extensive meteorological data from both ground-based and satellite observations, ensuring broader spatial coverage. In Zambia, medium- and short-term forecasts are generated by analyzing global models that ingest only a limited number of stations’ surface observation data, despite ground-based data being considered the gold standard in forecasting. This research leverages a Machine Learning algorithm to improve rainfall prediction accuracy. Historical monthly data from 41 manual weather stations (1981–2008) were used to train a NeuralProphet model, while data from 2009–2015 were employed for validation. Using the manual station data, forecasts for 2016–2022 achieved an average Mean Absolute Percentage Error (MAPE) of 37.86% and an accuracy of 62.14%. Incorporating daily reanalysis data from 116 districts reduced the MAPE to 29.34% and increased accuracy to 70.66%. Further integration of daily data from 20 Automatic Weather Stations reduced the MAPE to 29.31% and improved accuracy to 70.69%. Ingestion of sea Surface Temperature further reduced the MAPE to 29.27% and increased accuracy to 70.73%. A final integration of satellite data from ECMWF and TAMSAT reduced MAPE to 28.39% and increased the accuracy to 72.61%. The forecasts demonstrate a 90% confidence level. These findings clearly show that ingesting more climate data significantly enhances rainfall forecast accuracy, thereby increasing its reliability for decision-making and planning purposes.