Malaria is a serious and sometimes fatal disease caused by a parasite that is transmitted through the bite of infected mosquitoes. Millions of people are impacted globally, especially in tropical and subtropical areas. A variety of tactics, including vector control, surveillance, and research into novel medications and vaccines, are used in the fight against malaria. Malaria continues to be a serious worldwide health concern despite recent great progress, especially in underdeveloped nations with low resources for prevention and healthcare. In this research, an in-depth time series analysis model was developed using Python libraries including Matplotlib, Pandas, and StatsModel for three different countries. The model was developed using WHO (World Health Organization) data. The AdFuller test was used to determine the stationary of the data. Also, Seasonal Decomposition was employed to break down a time series into its constituent components, typically trend, seasonality, and random noise. After several other pre-processing steps, an ARIMA and SARIMA model was developed to predict future values for the number of cases and deaths due to Malaria. Both the models were evaluated using metrics like MSE (Mean Squared error), MAE (Mean Absolute error), and RMSE (Root Mean Squared error). Further, a Statistical approach was considered, and compared to the ARIMA and SARIMA models. Developing a statistical model was meant to evaluate the accuracy of the predictions provided by the ARIMA and SARIMA models.