We developed and applied a focused AI approach to model the time-on-stream evolution of the measured reactivity of palladium-based alloys synthesized mechanochemically and applied in the selective hydrogenation of equally concentrated acetylene-ethylene streams. Out of 21 measured and calculated candidate descriptive parameters reflecting the reaction conditions and materials' mesoscopic, bulk, and (sub)surface properties, we identified key physical parameters correlated with the reactivity. The key materials' parameters identified by AI highlight the crucial influence of surface and subsurface carbon and hydrogen chemistry on the selectivity, among other underlying processes. Guided by the AI models, new bimetallic and trimetallic alloys were designed and tested experimentally. The materials proposed by AI and tested experimentally present ethylene selectivity comparable to the best performing catalysts in the training set. Besides, the AI models predict the evolution of their reactivity with time on stream with good accuracy. Further investigations center currently on the mechanochemical synthesis of immiscible Pd-Ru alloys which are predicted by the AI models to outperform the rather typical Pd-Ag systems in this reaction. In fact, mechanochemistry might enable the synthesis of immiscible systems, e.g., Pd-Pt and Pt-Au. The focused data-centric approach introduced in this paper allows to navigate across immense pools of possible catalytic materials while taking into account their time-on-stream behavior, making it a valuable tool in catalyst and process design.