The Indian Ocean Dipole (IOD) is a climate phenomenon that affects weather patterns, causing droughts and floods in surrounding regions. This study uses a machine learning model (SVM) to forecast IOD by analyzing ocean and atmosphere data, such as sea surface temperature, wind patterns, and ocean heat. Advanced tools like SHAP helps to explain how these factors influence IOD and long-term climate changes. The model also maps physical processes to show why IOD events occur. Early results highlight the importance of ocean heat and winds in improving predictions. By combining statistical methods with physical insights, this research could lead to better long-term climate forecasts, helping communities mitigate IOD-related risks.