The rapidly evolving landscape of cryptanalysis necessitates an urgent and detailed exploration of the high-degree non-linear functions that govern the relationships between plaintext, key, and encrypted text. Historically, the complexity of these functions has posed formidable challenges to cryptanalysis. However, the advent of deep learning, supported by advanced computational resources, has revolutionized the potential for analyzing encrypted data in its raw form. This is a crucial development, given that the core principle of cryptosystem design is to eliminate discernible patterns, thereby necessitating the analysis of unprocessed encrypted data. Despite its critical importance, the integration of machine learning, and specifically deep learning, into cryptanalysis has been relatively unexplored. Deep learning algorithms stand out from traditional machine learning approaches by directly processing raw data, thus eliminating the need for predefined feature selection or extraction. This research underscores the transformative role of neural networks in aiding cryptanalysts in pinpointing vulnerabilities in ciphers by training these networks with data that accentuates inherent weaknesses alongside corresponding encryption keys. Our study represents an investigation into the feasibility and effectiveness of employing machine learning, deep learning, and innovative random optimization techniques in cryptanalysis. Furthermore, it provides a comprehensive overview of the state-of-the-art advancements in this field over the past few years. The findings of this research are not only pivotal for the field of cryptanalysis but also hold significant implications for the broader realm of data security.