A B S T R A C T

The purpose of the research - is to explore how AI-driven optimization can enhance the efficiency, reduce costs, and promote sustainability in commercial transport. The study focuses on improving logistics operations, decision-making, and resource management to support sustainable practices in the transport sector.  

The methodology of the research – the research uses predictive analytics and comparative analysis to assess the impact of AI-driven optimization on logistics efficiency and sustainability. Data is analyzed to evaluate improvements in fuel consumption, route optimization, and overall operational performance.

The practical importance of the research - the findings demonstrate that AI-driven logistics optimization significantly reduces fuel use, operational costs, and carbon emissions. By enhancing decision-making, minimizing delays, and improving resource utilization, AI contributes to more sustainable and cost-effective commercial transport. 

The results of the research - the research reveals that AI implementation leads to measurable improvements in fuel efficiency, route accuracy, and reduced environmental impact. Companies using AI-driven systems experienced a significant reduction in delivery times and operational expenses. 

The originality and scientific novelty of the research - this research uniquely analyzes the importance of AI not just in operational efficiency, but as a core driver of environmentally responsible supply chain strategies. 

Keywords: artificial Intelligence, logistics optimization, commercial transport, sus­tainable supply chain, smart transportation, predictive analytics.

http://doi.org/10.59610/bbu2.2025.2.8