In this study, the effects of climate change on the energy consumption and thermal comfort of the education building were examined and the effectiveness of passive improvement scenarios based on window parameters was evaluated by machine learning and statistical analysis. The research was based on a four-stage approach based on building simulations: (i) creation and analysis of climate change scenario-modified climate datasets, (ii) climate change impact analysis on existing building, (iii) comparative analysis of improvement scenarios, and (iv) analysis of predictive models based on machine learning. An existing secondary school building in Ankara was chosen as a case study for the evaluation of the selected performance indicators. 2025 scenarios were parametrically modeled with varying window parameters. After analyzing the complete dataset generated from performance simulations with descriptive statistics, Random Forest (RF) prediction models are trained with a subset of the data for each performance indicator. For each performance indicator, the feature importance of fine-tuned RF models was calculated with 10-fold cross-validation method, and it was seen that the window SHGC value was the most critical parameter among the tested variables. Performance predictions with RF models deviate 2% on average from their actual values and imply high predictive capacity. Moreover, with the retrofit scenarios, total energy consumption showed a reduction of up to 50%, whereas a significant improvement in indoor thermal comfort was observed. The results emphasize that the right selection of window parameters in existing educational buildings has a great effect on building energy performance. The results show that machine learning can be used effectively in the adaptation processes of buildings to climate change. The method used can be extended to cover different building parameters and technologies.