Quantum computing threatens classical cryptographic methods such as RSA and ECC, which rely on problems solvable by algorithms such as Shor’s. Post-quantum cryptography (PQC), particularly lattice-based systems based on Learning with Errors (LWE) and the Shortest Vector Problem (SVP), offers strong resistance but faces challenges of large key sizes, high computational costs, and limited practicality. This paper proposes a framework that integrates machine learning (ML) with lattice-based cryptography to address these issues. Supervised learning is applied for parameter tuning, while reinforcement learning supports adaptive key generation. The model reduces key sizes, improves encryption and decryption speed, and lowers computational overhead without compromising quantum resistance. Formal security proofs demonstrate that the integration of ML does not diminish the difficulty of LWE or SVP. Comparative results indicate that ML-enhanced lattice systems achieve better scalability, efficiency, and practicality than standard PQC methods, particularly in resource-constrained environments such as IoT. These findings demonstrate the potential of ML to strengthen PQC and advance secure cryptographic solutions in the quantum era.