The rapid expansion of software systems and digital services has led to a noticeable rise in global energy usage, which in turn contributes to increasing carbon emissions and environmental concerns. Although modern computing technologies have greatly improved efficiency, speed, and scalability, the environmental impact of software applications often remains overlooked and insufficiently examined. As a result, the true ecological cost of software operations is not clearly visible to developers or decision-makers. This study introduces an Explainable Artificial Intelligence (XAI)–based framework designed to assess, evaluate, and interpret the environmental impact of software applications, focusing specifically on energy consumption and carbon footprint. The proposed framework combines energy monitoring tools, system performance indicators, and machine learning models to estimate environmental costs under varying software workloads. By incorporating explainable AI methods, the framework offers clear and understandable insights into how individual code segments, algorithms, and runtime behaviors influence overall energy consumption. This level of transparency helps developers identify energy-intensive operations and supports informed decision-making for developing more sustainable and energy-efficient software solutions. To validate the framework, a prototype system is implemented and tested using standard benchmark applications. The experimental results demonstrate the framework’s capability to deliver reliable energy predictions along with meaningful interpretability. The findings emphasize the role of explainable models in aligning software performance optimization with environmental sustainability. Overall, this research provides a practical pathway for advancing green software engineering practices and encouraging the development of environmentally responsible computing systems.