Accurate energy consumption prediction is crucial for efficient travel planning and reliable driving range estimation, particularly for low-resource electric vehicles. Energy consumption is influenced by several dynamic factors, such as driving behavior, road topology, vehicle load, and environmental conditions, making precise forecasting a challenging task. In this paper, we propose an integrated framework that systematically incorporates these key influencing factors to improve the reliability of forecasting energy consumption for upcoming trips. Our approach leverages machine learning techniques to model complex relationships between these variables, enabling more reliable remaining range estimation. To assess the performance of the proposed framework, extensive experiments were conducted using a public real-world dataset. The results demonstrate the robustness of our approach, achieving an R-squared value of up to 95.2%, highlighting its potential for improving energy management strategies and increasing user confidence in electric vehicle adoption. Furthermore, our framework can be adapted to various driving conditions, offering a scalable and practical solution for future intelligent transportation systems.