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Data Envelopment Analysis (DEA) stands out as the most commonly employed approach for assessing the overall performance of a group of similar Decision-Making Units (DMUs) that utilize similar resources to produce comparable outputs. Nonetheless, the observed characteristics of symmetry or asymmetry in various types of data in real-world applications can often be imprecise, unclear, insufficient, or contradictory. Neglecting these conditions can potentially result in erroneous decision-making. Certain models take a more restrictive approach by assuming that inputs and outputs possess the same level of determinism. Regrettably, such constraints don't hold true for the majority of real-world scenarios. In actual situations, however, the observed input and output data may sometimes be neutrosophic numbers. So, the primary purpose of this study is to construct a Neutrosophic Input Oriented DEA (NIODEA) Model that incorporates both neutrosophic and deterministic output and/or input variables, handled in accordance with the scoring function. The model we have developed has broad applicability across diverse organizations, aiding decision-makers in making informed choices and optimizing resource allocation, a particularly valuable asset in today's intensely competitive business environment. To underscore the practical utility of the model, we provide an illustrative example that demonstrates its effectiveness and relevance for decision-makers.