SmartFuel: Fuel Consumption Prediction Model Using Machine Learning

Abstract

Fuel consumption is a critical factor in 
transportation efficiency, environmental impact, and 
cost 
management. Traditional methods for 
estimating fuel usage rely heavily on manual 
observations or fixed parameters, which are often 
inaccurate and lack adaptability. This project 
presents SmartFuel, a machine learning-based fuel 
consumption prediction system designed to deliver 
accurate, real-time insights using historical and 
operational vehicle data. 
SmartFuel utilizes regression algorithms to model 
fuel consumption based on variables such as engine 
size, vehicle weight, fuel type, and driving patterns. 
The system is trained on a real-world dataset and 
implemented using Python, Flask (for the backend), 
and HTML/CSS for the web interface. The 
application provides users with a simple interface to 
input vehicle parameters and receive precise fuel 
consumption predictions, enabling better planning, 
cost reduction, and environmental responsibility. 
This project demonstrates the power of predictive 
analytics in transportation and offers a scalable 
solution for vehicle efficiency monitoring. With 
potential applications in fleet management, logistics, 
and sustainable driving, SmartFuel represents a step 
toward smarter, data-driven fuel usage decisions.