This paper investigates a new class of generalized variable-order stochastic fractional Langevin equations involving the (ϱ,χ)–Caputo fractional derivative with Mittag–Leffler type non-singular kernel. The proposed model extends classical fractional Langevin systems by incorporating adaptive memory effects through variable-order fractional operators and generalized kernel structures. Using fixed-point techniques, including the Banach contraction principle and Krasnoselskii’s theorem, sufficient conditions for the existence and uniqueness of solutions are established. Further, stochastic Mittag–Leffler stability of the considered system is analyzed with the aid of generalized fractional Gronwall inequalities. In addition, several qualitative properties associated with stochastic perturbations, adaptive memory behavior, and nonlocal effects are discussed. To demonstrate the applicability of the theoretical results, an illustrative example together with extensive numerical comparison analysis is presented. Various computational schemes such as finite difference, predictor–corrector, spectral, hybrid operational matrix, and adaptive wavelet-based methods are compared in terms of convergence rate, computational efficiency, stability index, synchronization accuracy, and numerical error behavior. The obtained results show that the proposed generalized variable-order stochastic fractional Langevin model provides improved stability, lower computational error, enhanced adaptive memory characteristics, and superior numerical performance compared with existing fractional Langevin formulations. The developed framework offers a robust mathematical tool for modeling complex stochastic dynamical systems with variable memory and nonlocal interactions arising in engineering, physics, and applied sciences.