<p>Intuitionistic fuzzy set (IFS), introduced by Atanassov, extends classical fuzzy sets (FSs) by incorporating both membership degree (MD) and non-membership degree (NMD), allowing for a more nuanced representation of uncertainty. N-bipolar soft set (NBSS) extends the traditional N-soft set (NSS) by incorporating bipolarity, while also supporting both binary and multinary evaluations. In this paper, we propose an intuitionistic fuzzy N-bipolar soft set (IFNBSS) model, a hybridization of IFS and NBSS that combines intuitionistic fuzzy elements, multinary evaluation, and bipolarity considerations. We define the fundamental operations of IFNBSS, establish their algebraic properties, and illustrate their applicability through examples. In addition, we embed IFNBSS into a multi-criteria decision-making (MCDM) scenario by providing a structured algorithm for determining the best alternative. A numerical example on the assessment of artificial intelligence (AI) in medical diagnosis illustrates the practical efficacy of the model on handling of uncertain and confounding information. We also demonstrate generality and robustness of IFNBSS through a detailed comparison with existing approaches.</p>