Real-world problems such as protecting critical infrastructure and cyber networks and protecting wildlife, fishery, and forest often involve multiple decision-makers. While game theory is an established paradigm for such problems, its applicability in practice is often limited by computational intractability in large games, the unavailability of game parameters and the lack of rationality of human players. On the other hand, machine learning has led to huge successes in various domains and can be leveraged to overcome the limitations of the game-theoretic analysis. In this talk, I will introduce our work on integrating machine learning with computational game theory for addressing societal challenges such as security and sustainability, covering the following directions: data-based game-theoretic reasoning, learning-powered strategy computation in large scale games, and end-to-end learning of game parameters.