Tropical cyclones, considered extreme weather events, can cause significant damage to coastal areas, impacting millions of people and animals while also posing the risk of substantial economic losses. Traditionally, the Dvorak technique has been employed to assess the intensity of these cyclones, involving the visual analysis of satellite data to evaluate the storm’s cloud patterns and strength. In recent years, various studies have explored the use of deep learning (DL) and machine learning (ML) techniques to estimate tropical cyclone intensity. However, there is a lack of research providing a comparative analysis that integrates both ML and DL approaches for the estimation of tropical cyclone intensity. This study looks into the use of ML and DL techniques to estimate the strength of tropical cyclones. On diverse datasets and satellite imagery, we study the usage of convolutional neural networks (CNN, VGG16, DenseNet), recurrent neural networks (LSTM), and other machine learning methods (XGBoost, CatBoost, SVM, DT). Our findings suggest that both ML and DL methods have substantial promise for improving tropical cyclone intensity estimation accuracy; however, in our case study, DL algorithms outperformed ML algorithms. This study investigates the utilization of ML and DL techniques in assessing the strength of tropical cyclones. Employing various datasets and satellite imagery, we examine the performance of convolutional neural networks (CNNs such as VGG16 and DenseNet), recurrent neural networks (LSTM), and other ML methods (XGBoost, CatBoost, SVM, DT). Our results indicate that both ML and DL approaches show significant promise in enhancing the accuracy of tropical cyclone intensity estimation. Nevertheless, in our specific case study, DL algorithms demonstrated superior performance compared to ML algorithms.