In recent years, there has been an increasing focus on improving vehicle fuel consumption, driven by concerns over rising fuel costs, health effects, and environmental considerations. The impact of sudden acceleration and deceleration (braking) on fuel consumption is indeed significant, with driver behavior playing a crucial role. However, accurately measuring all contributing factors poses substantial challenges, resulting in uncertainties. Researchers and experts have made many efforts in this field, relying on various methods to deal with some challenges. Recently, machine learning (ML) methods have become powerful resources for monitoring and understanding how drivers’ behaviors affect fuel consumption. This book chapter aims to comprehensively review the relationship between driver behavior and fuel consumption, emphasizing the role of ML techniques. In particular, ML methods are reviewed, highlighting the relationships between driver behavior, fuel consumption, and CO2 emissions. Further, we discuss the potential of ML methods to enable stakeholders such as investors, car manufacturers, and transportation officials with tools for research-informed decisions. Lastly, remarks on the utilization of ML approaches to facilitate the optimization of fuel consumption and the development of a more efficient transportation system are discussed, aligning with the goals of enhanced decision-making and uncertainty management.