reaction (OER) [8][9][10] at the anode. Several studies have reported the improvement in the catalytic performance of each reaction; still a simplified system as well as improvement in the overall water splitting performance of a full cell have been continuously proposed. [11,12] Moreover, the use of different catalysts for HER and OER can lead to cross-contamination of catalysts, thereby affecting the overall performance and stability of the reaction. [13,14] Therefore, bifunctional catalysts, which can be applied for efficient HER and OER, are investigated. Among various materials, multimetallic alloys such as RuNiCo, [15] IrNiCu, [16] AlNiCoIrMo, [17] and CoFeLaNiPt, [18] have showed high performances. These multimetallic alloys have been investigated considerably in catalysis society due to several advantages. Compared to monometallic catalysts, multimetallic alloy catalysts demonstrate an unexpected behavior or a synergistic effect. [19] In addition, expensive noble metals can be substituted by cheaper non-noble metals while still exhibiting comparable performance. [20,21] Meanwhile, few studies have determined the optimal content of bifunctional catalysts by tuning their components and composition.For these reasons, finding an optimal balance between the participating reactions, and thus designing multimetallic alloys Design of bifunctional multimetallic alloy catalysts, which are one of the most promising candidates for water splitting, is a significant issue for the efficient production of renewable energy. Owing to large dimensions of the components and composition of multimetallic alloys, as well as the tradeoff behavior in terms of the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) overpotentials for bifunctional catalysts, it is difficult to search for high-performance bifunctional catalysts with multimetallic alloys using conventional trial-and-error experiments. Here, an optimal bifunctional catalyst for water splitting is obtained by combining Pareto active learning and experiments, where 110 experimental data points out of 77946 possible points lead to effective model development. The as-obtained bifunctional catalysts for HER and OER exhibit high performance, which is revealed by model development using Pareto active learning; among the catalysts, an optimal catalyst (Pt 0.15 Pd 0.30 Ru 0.30 Cu 0.25 ) exhibits a water splitting behavior of 1.56 V at a current density of 10 mA cm −2 . This study opens avenues for the efficient exploration of multimetallic alloys, which can be applied in multifunctional catalysts as well as in other applications.