Burning fossil fuels like coal, oil, and natural gas releases significant CO2, driving carbon emissions in electricity production. This contributes to global warming, leading to climate change, extreme weather, rising sea levels, and harm to ecosystems and human health. Achieving zero emissions requires transitioning to low-carbon energy sources. This study uses various Machine Learning (ML) models for predicting low carbon electricity generation additionally eXpalinalbe Artificial Intelligence (XAI) to elucidate how ML model works and suggest some important factor contributing to the models outcome. Among several ML models RF outperforms other by achieving MSE of 2.782, RMSE of 2.782, MAE of 1.443 and R2 of 99.3%. Also GBM and XGB performed closely as RF. When applying XAI tools like SHAP and Shapash to RF it reveals some important factors such as Electricity from renewables (TWh), Eectricity from fussil fuels (TWh) Renewable energy share in the total final energy consumption (%), and Electricity from nuclear. Which are crucial for future energy planning and policy decisions. Future study can use a more extensive data set, investigate economic aspects, and include real-time data to enhance predictive model performance