Storm surge elevates the water level, resulting in floods that cause significant harm to our lives and property. Predicting possible occurrences of storm surge using machine learning technology has long piqued the interest of the different communities. However, it is observed that the accuracy rate of predictions is insufficient. This paper aims to develop a framework for evaluating the performance of various machine learning models. Various algorithms are being used to forecast storm surge incidents in Hong Kong and Kaohsiung. The inclusion of trend changes in atmosphere pressure and wind components throughout the implementation of the algorithms to the dataset gathered over the past is one of the innovative aspects of this work. These inclination characteristics are very vital and crucial to capture important correlations and features for the potential occurrence of storm surges. This study yields two major findings. First, even the basic method of an ensemble algorithm produces better results than any single algorithm. Second, determining the best performing ensemble models for those storm surge events is achievable based on the technique findings for the data analyzed.