Within the China standard (6061 GB/T 3190-2008) of the aluminum alloy 6061, there are a wide range of alloy compositions having multiple trace elements. From the viewpoint of scientific research and quality control in industries, it is important to understand the relationship between the different potential compositions and corresponding mechanical properties of the aluminum alloy 6061. In this work, high-throughput experiments on materials synthesis and an active-learning framework based on the Bayesian optimization sampling process were combined to develop effective machine learning (ML) models to describe the relationship between the composition and hardness of aluminum 6061 alloys. In this work, > 100 alloys with ML designed compositions were synthesized and their hardness data were obtained through high-throughput experiments. The composite ML features were introduced by combining elementary material properties and chemical compositions of alloys and were selected subsequently according to their importance and correlation among features. The efficiencies of two sampling strategies were compared in guiding the iterative experiments: manual sampling based on empirical experience and Bayesian optimization sampling trained within the active-learning framework using the efficient global optimization and knowledge gradient algorithms. These ML models were updated iteratively until the prediction accuracy approached the experimental error. Specifically, the error in the hardness values predicted by the Bayesian model using 64 aluminum alloy samples after three rounds of iterations was 4.49 HV (7.23%), which is much lower than the error predicted by the empirical sampling method (9.73 HV; 15.68%). The results show that Bayesian optimization sampling accelerates the optimization of alloys property more efficiently than manual empirical sampling. Finally, the machine learning models using Bayesian sampling were interpreted using the Shapley additive explanations method and analysis of the partial dependence plot discuss the effects of various trace alloying elements and composite ML features on the hardness of the aluminum alloys. It was found that the hardness value of the aluminum alloys became large when the ratio between Mg and Si (Mg/Si) was between 1.37 and 1.72. In addition, the machine learning models suggested that the lattice distortion, cohesive energy, configurational entropy, and shear modulus were positively proportional to the hardness of the alloy. This work demonstrated that active-learning-guided high-throughput experiments on composition refinement can not only improve the performance and quality control of aluminum 6061 alloys within its standard nominal composition range as used in industry but also provide a feasible approach for the design and property optimization of other multialloy materials.