Climate change poses significant challenges to reservoir operations by altering precipitation patterns, increasing evaporation rates, and affecting the overall water availability. Changes in precipitation intensity and frequency have led to unpredictable inflow patterns, thus complicating reservoir management strategies. These unpredictable inflow patterns can lead to challenges in maintaining water supply reliability, hydropower generation, and ecosystem health. Thus, accurate inflow prediction under climate change scenarios is crucial for optimizing reservoir operations. The primary objective of this study was to evaluate the impact of climate change on the Idukki Reservoir inflow. First, inflow prediction models were developed using techniques such as Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) using historical climatic and reservoir data from 2000 to 2022. Through rigorous evaluation, the RF model emerged as the most promising model for future predictions. Subsequently, utilizing the trained RF model, the future reservoir inflow from 2023 to 2100 was forecasted for different emission scenarios. The results indicate a substantial increase in inflow to the reservoir from August to February in the future compared to historical periods. Overall, our findings suggest a significant increase in reservoir inflow in the future, highlighting the importance of proactive water resource management strategies in the face of climate change.