A new methodology employing deep neural networks models and empirically tests the security of key encapsulation mechanisms (KEMs), hybrid constructions, and cascade encryption schemes. Simon Calderon and colleagues at Linköping University apply this deep learning framework to public-key encryption schemes including ML-KEM, BIKE, and HQC, as well as combinations with classical algorithms like RSA and AES. The methodology offers a flexible approach to data-driven validation. The research confirms these algorithms and combinations currently exhibit no key vulnerabilities under the tested conditions, aligning with established theoretical security guarantees and offering a vital new set of tools for practical cryptographic analysis. Deep learning sharply enhances empirical validation of post-quantum cryptographic An 80% reduction in ciphertext distinguishing accuracy for HQC.pke resulted from employing a deep learning approach, exceeding the previously achievable 20% error rate and resolving a key limitation in validating post-quantum cryptography. This improvement enables empirical testing of complex hybrid encryption schemes, previously impossible with methods reliant on theoretical guarantees alone. Traditional analysis struggled to assess combinations of new and established cryptographic techniques, hindering comprehensive security evaluations. Deep learning sharply enhances empirical validation of post-quantum cryptography by providing a more robust method for assessing these complex systems. The deep learning framework models the IND-CPA game, a standardised security test, as a binary classification task, allowing for data-driven validation of implementations and compositions. Further validation of these findings came from applying the framework to cascade symmetric encryption, testing combinations of AES-CTR, AES-CBC, AES-ECB, ChaCha20, and DES-ECB; this demonstrated the flexible nature of the approach beyond post-quantum algorithms. This adaptive testing method complements analytical security analysis, offering a flexible tool for assessing the security of evolving cryptographic systems and their combinations. Statistical analysis, utilising two-sided binomial hypothesis testing, confirmed that no tested algorithm or combination achieved a statistically significant advantage over random guessing, aligning