This dissertation centers on the sustainable design and optimization of shale gas energy systems with mathematical programming models and tailored solution algorithms. Specifically, three research aims are proposed. The first aim is developing integrated optimization models for the sustainable design and operations of shale gas energy systems, which covers three detailed projects. In the first related project, we propose an endpoint-oriented life cycle optimization framework to investigate the economic and environmental implications of incorporating modular manufacturing into shale gas supply chains. In the second project, we develop an integrated hybrid life cycle optimization (LCO) model, which enables automatic identification of sustainable alternatives in the design and operations of shale gas supply chains. In the third project, we propose a dynamic MFA-based LCO framework that provides high-fidelity modeling of complex material flow networks with recycling options and enables detailed accounting of time-dependent life cycle material flow profiles. The second aim centers on hedging against uncertainty in the shale gas energy system with special focus on data-driven stochastic optimization approach. In the corresponding project, we address the optimal design and operations of shale gas supply chains under uncertainty of estimated ultimate recovery (EUR). The third aim addresses the modeling and optimization of decentralized shale gas energy systems. We propose a novel game-theory-based stochastic model that integrates two-stage stochastic programming with a single-leader-multiple-follower Stackelberg game scheme for optimizing decentralized supply chains under uncertainty.