This study explores the complex dynamics of unsteady convective flow in micropolar nanofluids over a rough conical surface, with a focus on the effects of triple diffusive transport and Arrhenius activation energy. The primary objective is to understand the interplay among nonlinear convection, micro-rotation effects, and species diffusion under the influence of thermal and electromagnetic forces. The analysis is motivated by practical applications of cryogenic fluids, specifically liquid hydrogen and liquid nitrogen, where precise control of heat and mass transport is critical. The conical surface roughness is mathematically modeled as a high-frequency, small-amplitude sinusoidal waveform. To address the non-similar nature of the boundary-layer equations, Mangler's transformations are employed, followed by the implementation of a finite difference scheme for numerical solutions. The methodology further integrates a machine learning-based neural network to predict the skin friction under the influence of roughness-induced perturbations, ensuring computational efficiency and improved generalization. The study yields several novel findings. Notably, the presence of surface roughness introduces the wave-like modulations in the local skin friction coefficient. It is also observed that nonlinear convective interactions, enhanced by temperature gradients and vortex viscosity parameters, significantly intensify near-wall velocity gradients. Moreover, key physical quantities are correlated with governing parameters using power-law relationships, providing generalized predictive models. The validation of the numerical results is achieved through consistency checks with the existing limiting solutions and convergence analysis, ensuring the reliability of the proposed computational framework.