Drastic change in climatic conditions is a very big and challenging task for people around the globe. Most of the biological, constructional, transportation and agricultural sectors get affected due to uneven weather conditions, i.e. flood, rainfall, drought, etc. As part of the weather system, rainfall being most prominent phenomena, its rate is treated as one of the most important variables. Meteorological scientists try to identify the parameters of the atmosphere such as temperature, sunshine, cloudiness and humidity of the earth by applying conventional techniques and developing a prediction model. These days, Machine Learning (ML) techniques are more evolving and give more accurate results than the traditional approaches. ML is a subset of artificial intelligence (AI) which is used in this paper for predicting the next day's rainfall from the past 10 year’s weather dataset of Australia. This paper presents the ML classifiers such as Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Light Gradient Boost Machine (LGBM), Cat Boost (CB), and Extreme Gradient Boost (XGB) to predict the rainfall of the next day. The Python software package having an inbuilt library like Pandas, Numpy Scikitlearn, and Matplotlib is extensively used for data management, mathematical computation, ML modeling, and visualization tools, respectively. This is followed by sequential stages of data visualization, training, testing, modeling, and cross-validation. The evaluation metrics like Area under the Receiver Operating Characteristic (AUROC) curve, recall, accuracy, precision, and Cohen kappa are used to check the performance of ML algorithms.