Rainfall forecasting is an essential area of interest not only in agriculture, but also in water resources and disaster preparedness. Aim: To study the impact of meteorological and hydroclimatic factors including temperature, humidity, wind, atmospheric pressure, and cloud cover on rainfall prediction. This study uses interaction effects with other environmental variables such as terrain variables, land use, and soil moisture to emphasize the challenges but also potential advances in predictive models. The study intends to develop enhanced rainfall predictions, which will add value to the fields of agriculture, water management, and climate-related domains, using advanced statistical and machine learning techniques. Understanding those interactions as well as individual factors holds the key to improving conclusions and prediction skill, opening the path to developing more robust methodologies about the risks of future environmental conditions.