As cyclones are among the most destructive natural disasters to human life, anticipating cyclone sensitivity is important in the management of disasters. The purpose of this paper is to advocate for the application of machine learning methods to the forecasting of cyclonic events based on wind speed, air pressure, temperature, and humidity as relevant factors. To counter the challenges of label noise, missing values, and class over-representation, and to estimate the issues present in real data, the acquired meteorological data is pre-processed. On accuracy, precision, recall, and ROC-AUC, for instance, the comparison of the Gradient Boosting and Logistic Regression models shows that the latter outperforms the former. The study focuses on the benefits of ensemble techniques in handling non-linear relationships and class imbalance conditions, and also the need for holistic preprocessing techniques such as noise elimination and feature scaling. The study confirms that machine learning (ML) applications will have a great potential to enhance cyclone forecasting systems, a significant step towards disaster preparedness and risk reduction. To enhance the quality of the model in the future, deep learning techniques will be tried out and blended with current data.