Significant progress has been achieved, in the field of marine science, which entails the complex study of the enormous ocean and its interrelated ecosystem through the incorporation of data science. This study focuses on the application of data science in marine science, particularly in predicting anomalies in sea surface temperature (SST). Accurately predicting SST anomalies is critical for understanding climate dynamics, oceanic currents, and the delicate balance of marine life. This study investigates current advances in SST anomaly prediction with data science through a meticulous analysis of research articles. It presents a thorough examination of machine learning techniques for forecasting SST anomalies, highlighting their advantages, disadvantages, and potential future directions. This study adds to our understanding of ocean behavior by fusing cutting-edge analytical techniques with a deep knowledge of SST anomalies. By critically analyzing previous studies and offering novel insights, this research contributes to advancing the field of marine data science and supports the sustainable management of marine resources. In this research endeavor, we introduce an innovative ensemble framework that amalgamates a meticulously designed 9-layer Long Short Term Memory (LSTM) network with the robust eXtreme Gradient Boosting (XGBoost) ensemble algorithm. By synergizing these two advanced methodologies, we aspire to propel the realm of marine data science towards greater horizons. Our approach distinctly contributes to the domain by substantially refining the prediction of SST anomalies, consequently culminating in a noteworthy enhancement in the precision of forecasts.