The rational design of advanced methanol oxidation reaction (MOR) electrocatalysts can significantly enhance the catalytic activity and performance of direct methanol fuel cells (DMFCs). Herein, the electrocatalysis informatics-assisted design electrocatalysts for MOR is firstly conducted by combining machine learning based on 616 experimental data points with first-principles calculations. Guided by this theoretical insight, a highly disordered PtRuPd alloy aerogel is prepared via a facile one-pot synthetic strategy. The obtained electrocatalyst demonstrates excellent mass activity of 2.42 A·mg_Pt⁻¹ and specific activity of 7.13 mA·cm⁻² for MOR, which is considerably higher than that of most Pt-based catalysts. The self-supported ultrathin anode catalyst layer (~6.3 μm) integrated into a membrane electrode assembly exhibits the mass-specific power density of 92.9 W·g_Pt⁻¹ at 65 °C for DMFC operation, surpassing that of recently reported Pt-based catalysts. This work offers a promising approach to exploring a digitalization and intelligent cross-scale design route for MOR electrocatalysts.