Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{189048,
        author = {Hardik Singh and Rajeev Kumar Gupta and Anshika Singh and Pradyuman Yadav and Aditya Rao and Vijyank Shekhar and Dhruv Chaubey and Taranveer Singh},
        title = {Machine Learning Approaches to the Collatz Conjecture: A Comprehensive Framework for Pattern Recognition and Automated Conjecture Generation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {4522-4529},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189048},
        abstract = {The Collatz conjecture represents one of the most intriguing unsolved problems in mathematics, where the simple iterative map T(n) = n/2 for even n and T(n) = 3n+1 for odd n has resisted formal proof despite extensive computational verification. Recent advances in artificial intelligence, particularly in sequence modeling, graph neural networks, and symbolic regression, offer unprecedented opportunities to approach this classical problem through data-driven methodologies. This paper proposes a comprehensive framework that combines rigorous mathematical analysis with state-of-the-art machine learning techniques to generate new insights into Collatz dynamics, predict convergence patterns, and potentially discover novel mathematical invariants that could advance toward formal proof.},
        keywords = {},
        month = {December},
        }