The transportation problems with various dimensions and objectives reflect the transportation difficulties encountered in real life. This study develops a complex framework for a fuzzy multi-choice multi-objective, multi-item five-dimensional transportation problem (FMCMOMI5DTP), emphasizing the complexity of transportation difficulties in practical scenarios. The parameters associated with objective functions in FMCMOMI5DTP are taken as fuzzy variables, while supply, demand, and conveyance capacity parameters are taken into account within a multi-choice setting. Through the use of binary variables, the multi-choice parameters within the constraints are transformed into single-choice parameters. While traditional optimization algorithms are commonly applied to address TPs, they may struggle to understand the intuition and judgment of decision-makers. To address the issue, in this paper, NSGA III is employed to produce solutions that consider multiple objectives at different levels while avoiding domination by others. Nevertheless, NSGA III requires an initial feasible population to produce the solutions. To tackle the problem, the paper proposes a systematic approach to efficiently generate an initial population for managing the FMCMOMI5DTP. Furthermore, a numerical example is used to validate the proposed model. The problem is solved using NSGA III and compared with other variants of the genetic algorithm.