This paper demonstrates the integration of Reinforcement Learning (RL) into quantum transpiling workflows, significantly enhancing the synthesis and routing of quantum circuits. By employing RL, the authors achieve near-optimal synthesis of Linear Function, Clifford, and Permutation circuits, up to 9, 11, and 65 qubits, respectively, while being compatible with native device instruction sets and connectivity constraints. The method is orders of magnitude faster than optimization methods such as SAT solvers and achieves significant reductions in two-qubit gate depth and count for circuit routing up to 133 qubits. The approach is efficient enough to be useful in typical quantum transpiling pipelines, setting the stage for further AI-powered enhancements of quantum computing workflows. The paper also discusses the training and inference processes of the RL-based circuit synthesis and routing methods, and provides benchmark results comparing the RL-based methods against existing heuristics and SAT solvers.This paper demonstrates the integration of Reinforcement Learning (RL) into quantum transpiling workflows, significantly enhancing the synthesis and routing of quantum circuits. By employing RL, the authors achieve near-optimal synthesis of Linear Function, Clifford, and Permutation circuits, up to 9, 11, and 65 qubits, respectively, while being compatible with native device instruction sets and connectivity constraints. The method is orders of magnitude faster than optimization methods such as SAT solvers and achieves significant reductions in two-qubit gate depth and count for circuit routing up to 133 qubits. The approach is efficient enough to be useful in typical quantum transpiling pipelines, setting the stage for further AI-powered enhancements of quantum computing workflows. The paper also discusses the training and inference processes of the RL-based circuit synthesis and routing methods, and provides benchmark results comparing the RL-based methods against existing heuristics and SAT solvers.