Cyclonic systems create severe dangers to people and buildings and environmental systems thus demanding precise real-time detection capabilities. Current cyclone detection methods use satellite imagery which analysed manually through human teams, however this process is time-consuming and prone to human error. The presented paper conducts an extensive exploration into machine learning techniques for cyclone detection purposes. By leveraging historical weather data, satellite imagery, and advanced computational models, this study propose a novel approach to enhance cyclone prediction accuracy. The paper reviews existing methodologies, details the proposed ML-based framework, and compares results against traditional approaches. Experimental evaluations demonstrate significant improvements in detection accuracy and prediction timelines, underscoring the potential of machine learning in meteorology.