This paper introduces an innovative approach to tackle the shortest path problem in an imprecise environment, wherein edge weights are characterized by Single valued neutrosophic triangular fuzzy numbers (SVNTFNs). Unlike traditional methods, which rely on deterministic edge weights, proposed methodology incorporates the uncertainty inherent in real-world scenarios. Key to our approach is the development of a neutrosophic fuzzy ordering mechanism tailored for comparing SVNTFNs. By leveraging this mechanism, we can effectively navigate the complex decision space and select the most suitable path while accommodating the varying degrees of optimism and pessimism exhibited by decision-makers. This ensures that our methodology is capable of addressing a wide range of risk preferences and uncertainties. To assess the efficacy of proposed methodology, a comprehensive numerical demonstration is provided. Through this demonstration, we highlight the dynamic nature of decision-making in uncertain environment and underscore the practical utility of the proposed methodology.