In today’s production systems, supply chains, and fast-paced sectors, transportation system optimization is essential to maintain cost-effectiveness, resource efficiency, and efficiency. In real-world situations, solving multi-objective problems – where competing objectives must be addressed simultaneously – is often necessary. The multi-objective linear fractional transportation problem (MOLFTP), the subject of this paper, uses objective functions represented as ratios of linear functions to simulate these trade-offs. These issues are ideal for simulating trade-offs in complex transportation networks because they have objective functions described as ratios of linear functions. This paper addresses the MOLFTP, where the objective functions are expressed as ratios of linear functions. MOLFTP arises in scenarios that require optimization of multiple conflicting goals within a transportation framework. We propose an advanced weighted heuristic method designed to effectively optimize ratio-based objectives, which provides a practical approach to solving such complex problems. To demonstrate the effectiveness of the proposed method, numerical examples are provided, and the results are compared with previously reported solutions for the same set of numerical problems. The comparisons highlight the superior performance and applicability of the proposed approach in dealing with MOLFTP. The results highlight the method’s potential to deliver better, more efficient solutions, underscoring its relevance to real-world transportation challenges across industries and logistics.