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Rainfall is still a prominent meteorological phenomenon that has piqued the interest of many scientists who have sought to forecast and quantify its future proportions. Malaysia has had multiple devastating floods in recent years. Rainfall forecasting is critical for watershed management applications, particularly flood warning systems. Peninsular Malaysia saw its most recent flood in December 2021, affecting seven states. In this work, we will use the Box-Jenkin model, exponential smoothing, and a hybrid model that combines the Box-Jenkin and exponential smoothing approaches to estimate rainfall in Senai, Johor. Senai, Johor, was chosen due to its favourable location midway between east and west Malaysia. As a result, no strong monsoons, such as those in the northeast or southwest, occurred. Mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and a graphical representation will be used to compare the performance of the models. The results show that the Holt-model Winter's performs the best in predicting rainfall in Senai, Johor. The hybrid model, which combines two conventional models, did not enhance prediction, proving that it is not always required to create a hybrid model. [ABSTRACT FROM AUTHOR]