The ordered weighted averaging (OWA) operator is a fundamental tool in decision-making processes, particularly under conditions of uncertainty, by aggregating inputs through a reordering mechanism based on predefined weights. Despite its utility, the classical OWA operator is limited in addressing complex decision scenarios characterized by uncertainty, indeterminacy, and inconsistency. This study introduces two innovative aggregation operators, the induced ordered weighted averaging operator under a neutrosophic environment (IOWAN) and its generalized form (GIOWAN), which integrate the ordering flexibility of induced OWA with the expressive power of neutrosophic sets. The proposed operators advance existing models by (i) enabling simultaneous aggregation of truth, indeterminacy, and falsity information, (ii) incorporating application-driven inducing functions for dynamic ordering, and (iii) offering a unified framework encompassing arithmetic, geometric, harmonic, quadratic, and extreme-case induced operators. We formally define the operators, establish their mathematical properties, and present several novel extensions, including interval-valued, bipolar, probabilistic, entropy-based, and time-dynamic neutrosophic versions. An illustrative case study of the economic assessment of Southeast Asian countries was performed using a methodology based on (GIOWAN) Operators and results show that rankings align with real-world economic data. Comparative analyses highlight its superior performance in modeling intricate decision dynamics. The proposed algorithms are effective in a group decision-making environment within uncertain domains, solving problems of uncertainty, complexity, and multidimensional information, including sustainable development, policy formulation, and healthcare decision analysis.