aspg-21-3893-1791475975 20261008161255 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Neutrosophic Science IJNS 2692-6148 2690-6805 2025 26 4 Wiem Abdelbaki College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait Multidimensional data cubes are essential components in data warehouses, enabling rich, OLAP-based analysis across dimensions such as time, location, and product category. However, the complexity that supports such analytical flexibility often leads to extreme sparsity—where the majority of cube cells remain empty or only partially filled. This sparsity can hinder the performance of downstream machine learning models, especially when valuable but infrequent patterns are lost during preprocessing. This paper introduces a neutrosophic-based framework for evaluating and managing sparse regions within OLAP cubes. Instead of treating all sparsity as noise, we propose a typology that distinguishes between three forms: semantic sparsity (expected and justifiable absences), non-informative sparsity (regions with little analytical value), and informative sparsity (sparse areas that still carry meaningful insights). Each substructure is modeled using neutrosophic logic, which assigns degrees of truth, indeterminacy, and falsity to reflect its analytical potential. A dedicated Neutrosophic Evaluation Algorithm is developed to classify each region using metrics such as semantic confidence, entropy, and a context-aware informativeness score. These metrics allow for nuanced decisions: preserving informative sparsity, eliminating irrelevant regions, and flagging ambiguous areas for further review. This approach shows how neutrosophic logic can offer a novel and effective way to handle sparsity in OLAP cubes, improving the relevance and robustness of machine learning pipelines trained on multidimensional data. 2025 42 49 3893 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJNS.260405 https://www.americaspg.com/journal/21/article/3893 https://www.americaspg.com/storage/81749821223.pdf