Language includes a set of rules, symbols, and some structures which people use to communicate with each other. An artificial intelligence technique named natural language processing (NLP) allows computers to understand and analyze the human language, which will turn into a meaningful conversation with people. The problems that are not solved by traditional computers are solved by quantum computing with the help of principles like quantum superposition, quantum entanglement, etc. Quantum compilers play a crucial role by converting abstract ideas into instructions. This paper discussed various optimization strategies, including gate-level optimization, circuit-level optimization, code-level optimization, and algorithm-level optimization, that aim to enhance the efficiency and accuracy of quantum circuits. The system examines emerging approaches like reinforcement learning, peephole optimization, noise-adaptive techniques, and contemporary methods like Paulihederal and QUILC, which are the boundaries of quantum compiler optimization. These approaches and their applications will lay the foundation for developing powerful quantum technologies, accelerating the quantum computing revolution.