This paper presents a Reinforcement Learning (RL)-based approach for efficient quantum circuit synthesis and transpilation, significantly improving the performance of quantum circuit synthesis and routing. The method achieves near-optimal results for Clifford, Linear Function, and Permutation circuits, up to 11, 9, and 65 qubits respectively, while being compatible with native device instruction sets and connectivity constraints. It is significantly faster than traditional optimization methods like SAT solvers and reduces two-qubit gate depth and count for circuit routing up to 133 qubits. The method is efficient enough to be integrated into practical quantum transpilation pipelines and sets the stage for further AI-powered enhancements in quantum computing workflows. The paper introduces an RL-based approach to synthesize various types of quantum circuits, including Clifford, Linear Function, and Permutation circuits. The RL-based synthesis method generates near-optimal results and directly compatible circuits with the native instruction set of the device, making it suitable for direct execution on quantum devices. The method balances circuit optimality, adaptability, and computational cost, making it a valuable tool for practical transpilation workflows. It is extended to circuit routing, offering significant improvements over existing heuristic techniques with much lower computational costs. The RL method is trained using a curriculum learning approach, where the difficulty of the target operators is dynamically adjusted during training. The RL agent learns to synthesize circuits by trial and error, guided by a reward function that encourages optimal gate selection. The method is tested on various quantum circuits, including Clifford and permutation circuits, and shows significant improvements in terms of gate count and depth compared to existing methods like SAT solvers and heuristic algorithms. For Clifford circuits, the method achieves near-optimal results with a 60% improvement in CNOT layers. For permutation circuits, the method achieves 100% optimality in terms of SWAP count and depth for 8-L and 65% for 12-O topologies. For circuit routing, the method significantly reduces CNOT depth and gate count compared to existing methods like BIP mapper and SABRE. The paper also presents results for circuit routing with RL, showing a 20% reduction in CNOT depth and a 10% improvement in CNOT count compared to BIP mapper. For larger circuits, the RL method produces circuits that are on average 40% shallower than the Qiskit SDK transpiler and with 10% lower two-qubit gate count. The method is also applied to EfficientSU2 circuits, showing optimal routing with minimal SWAP overhead. The paper discusses the practicality and efficiency of the RL-based approach, highlighting its ability to scale to larger circuits and its potential for integration into real quantum computing environments. The method is shown to be effective across various transpilation tasks, including circuit synthesis and routing, and is capable of producing high-quality results with significantly lower computational costs compared to traditional optimization methods. The resultsThis paper presents a Reinforcement Learning (RL)-based approach for efficient quantum circuit synthesis and transpilation, significantly improving the performance of quantum circuit synthesis and routing. The method achieves near-optimal results for Clifford, Linear Function, and Permutation circuits, up to 11, 9, and 65 qubits respectively, while being compatible with native device instruction sets and connectivity constraints. It is significantly faster than traditional optimization methods like SAT solvers and reduces two-qubit gate depth and count for circuit routing up to 133 qubits. The method is efficient enough to be integrated into practical quantum transpilation pipelines and sets the stage for further AI-powered enhancements in quantum computing workflows. The paper introduces an RL-based approach to synthesize various types of quantum circuits, including Clifford, Linear Function, and Permutation circuits. The RL-based synthesis method generates near-optimal results and directly compatible circuits with the native instruction set of the device, making it suitable for direct execution on quantum devices. The method balances circuit optimality, adaptability, and computational cost, making it a valuable tool for practical transpilation workflows. It is extended to circuit routing, offering significant improvements over existing heuristic techniques with much lower computational costs. The RL method is trained using a curriculum learning approach, where the difficulty of the target operators is dynamically adjusted during training. The RL agent learns to synthesize circuits by trial and error, guided by a reward function that encourages optimal gate selection. The method is tested on various quantum circuits, including Clifford and permutation circuits, and shows significant improvements in terms of gate count and depth compared to existing methods like SAT solvers and heuristic algorithms. For Clifford circuits, the method achieves near-optimal results with a 60% improvement in CNOT layers. For permutation circuits, the method achieves 100% optimality in terms of SWAP count and depth for 8-L and 65% for 12-O topologies. For circuit routing, the method significantly reduces CNOT depth and gate count compared to existing methods like BIP mapper and SABRE. The paper also presents results for circuit routing with RL, showing a 20% reduction in CNOT depth and a 10% improvement in CNOT count compared to BIP mapper. For larger circuits, the RL method produces circuits that are on average 40% shallower than the Qiskit SDK transpiler and with 10% lower two-qubit gate count. The method is also applied to EfficientSU2 circuits, showing optimal routing with minimal SWAP overhead. The paper discusses the practicality and efficiency of the RL-based approach, highlighting its ability to scale to larger circuits and its potential for integration into real quantum computing environments. The method is shown to be effective across various transpilation tasks, including circuit synthesis and routing, and is capable of producing high-quality results with significantly lower computational costs compared to traditional optimization methods. The results