Neutrosophic sets are characterized by membership, non-membership and indeterminacy functions that provide a robust method for modeling of the incomplete or inconsistent data, which is commonly encountered in real-world decision-making scenarios. This paper proposes a multicriteria decision-making (MCDM) approach for handling the uncertain and vague information in decision problems using the aggregation operators and score functions within the framework of Neutrosophic Sets (NS). The proposed approach combines the aggregation operators to fuse the multiple criteria and alternatives along with the score functions to rank and evaluate the best alternatives. The aggregation operators are designed to combine the Neutrosophic sets associated with each criterion into a single comprehensive evaluation while the score function helps in deriving a crisp ranking of alternatives. A realistic example is provided to illustrate the approach's efficacy, showcasing its applicability in handling the complex decision problems under uncertainty and imprecision. On the basis of scoring functions and criteria, the table compares several aggregation functions. Existing approaches from Sachin, Garg, and Nafei et al. are contrasted with the suggested score function. The values of 0.233 and 0.333 produced by the suggested scoring function are similar to those of Sachin and Garg but lower than those of Nafei et al. Three criteria ( \({C}_{1},{C}_{2},{C}_{3}\) ) are included in the comparison for the aggregation functions for two options ( \({A}_{1},{A}_{2}\) ). The aggregated values for \(({A}_{1})\) are 0.3268, 0.2000, and 0.3881 when using the current aggregation operator (Ye [9]); however, the suggested aggregation operator produces 0.1796, 0.1056, and 0.1634, suggesting a decrease in values. Likewise, for \({(A}_{2})\) the suggested approach yields 0.5627, 0.1414, and 0.2000 using the current aggregation operator. These results suggest that this method offers a flexible and effective tool for decision makers in a situation involving incomplete or conflicting information.