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Crystal structure prediction remains a fundamental challenge in materials science, particularly in crystallography and solid-state physics, where identifying stable configurations under varying thermodynamic conditions is essential for the design of functional materials. Traditional methods that rely on ab initio calculations or evolutionary algorithms often struggle with the vast configurational space and complex constraints such as temperature, pressure, and phase equilibria. This paper proposes a new conceptual framework that combines reinforcement learning (RL) with thermodynamic principles to enhance the efficiency and accuracy of the crystal structure search. In conceptualizing the search process as a Markov decision process, the framework uses an RL agent to navigate structural modifications, guided by rewards derived from thermodynamic stability metrics such as Gibbs free energy and entropy contributions. The synthesis of literature shows that while machine learning has accelerated predictions, RL’s adaptive learning provides untapped potential for handling multifaceted constraints. The proposed model includes multi-objective optimization to balance stability and formation feasibility, avoiding reliance on empirical data. This purely theoretical approach fosters originality by redefining state-action spaces to embed symmetry and lattice constraints inherently. Implications extend to high-entropy alloys and polymorphic materials, potentially revolutionizing computational materials discovery. Through textual depiction of a conceptual diagram, the framework’s modularity is highlighted, enabling future extensions to quantum-informed rewards. Overall, this work bridges AI and thermodynamics, paving the way for conceptually robust, constraint-aware structure searches in applied artificial intelligence for materials science.