The global transport sector, a significant contributor to energy consumption and greenhouse gas (GHG) emissions, requires innovative solutions to meet sustainability goals. Artificial intelligence (AI) has emerged as a transformative technology, offering opportunities to enhance energy efficiency and reduce GHG emissions in transport systems. This study provides a comprehensive review of AI’s role in optimizing vehicle energy management, traffic flow, and alternative fuel technologies, such as hydrogen fuel cells and biofuels. It explores AI’s potential to drive advancements in electric and autonomous vehicles, shared mobility, and smart transportation systems. The economic analysis demonstrates the viability of AI-enhanced transport, considering Total Cost of Ownership (TCO) and cost-benefit outcomes. However, challenges such as data quality, computational demands, system integration, and ethical concerns must be addressed to fully harness AI’s potential. The study also highlights the policy implications of AI adoption, underscoring the need for supportive regulatory frameworks and energy policies that promote innovation while ensuring safety and fairness.

The article presents the results of measurements of the geometric structure of the surface after micro end-milling. In the experiments, coated monolithic super micrograin cemented carbide micro end mills with 2 flutes were employed. The machined parts were additively made of CoCr alloy using Selective Laser Melting technology (SLM). Analyzed variables were the volumetric density of energy supplied by the laser during the SLM process and the feed rate during micro-milling. The results showed a strong influence of SLM process parameters on the surface roughness, which, according to the authors, results from the significant variability of the mechanical properties of the material as a function of the volumetric density of energy supplied during melting.