Machine learning has become increasingly important in materials design, yet traditional single-task learning (STL) models fail to fully exploit the potential of available data in scenarios involving multiple targets and incomplete datasets. While STL models overlook the inherent correlations between target properties, this study showcases how multi-task learning (MTL) effectively leverages these correlations. Therefore, the performance of MTL methods compared to STL is evaluated across five datasets, covering twelve prediction tasks and incorporating different types and levels of data sparsity. Our findings reveal that MTL significantly outperforms STL, particularly in sparse data scenarios, with up to 15% prediction improvements across all tasks. Moreover, MTL methods utilizing regressor chains with automated machine learning tools achieve superior performance compared to those based on neural networks, with minimal training effort required. This work advances data efficiency in data-driven materials design, establishing MTL as a potent tool for simultaneous learning and predicting multiple material properties.