Exploring the historical evolution of olive cultivation, this research investigates the complex relationship between climate variations and olive dynamics. This work presents a comprehensive analysis of olive influx prediction based on a meticulously compiled dataset derived from climate change studies in the Levant. The 269 data points in the dataset include a variety of variables, including age prior to present, precipitation levels, and monthly temperatures. Through strategic pre-processing and model evaluation, the proposed ensemble approach emerges as the best model for predicting olive influx, outperforming other regression methods. Using SHAP analysis, key climate factors such as temperature, precipitation, and tree age are identified, shedding light on their importance to olive dynamics. Furthermore, this study goes beyond model performance, delving into a historical dataset to investigate the complex relationship between climate fluctuations and olive production. This research not only advances knowledge of the dynamics of olives in Tyre, Lebanon, but also offers a useful framework for other areas facing climate-related challenges in olive cultivation. This work bridges the gap between agricultural sustainability and machine learning, providing crucial insights for generating resilience in olive-growing regions addressing climate-related issues.