Published Paper Details:

Prime number distribution has been a subject of extensive mathematical exploration, with profound implications in number theory, cryptography, and computational mathematics. Traditional analytical approaches often rely on deterministic algorithms and conjectures, such as the Prime Number Theorem and Riemann Hypothesis. However, recent advancements in machine learning (ML) offer novel perspectives for predicting prime number distributions using data-driven methodologies. This study explores advanced ML-driven regression models to analyze and predict the occurrence of prime numbers within numerical sequences. Various regression techniques are employed to approximate underlying patterns in prime distributions. Comparative evaluations based on accuracy, generalization ability, and computational efficiency highlight the most effective models for prime number prediction. The findings contribute to the growing intersection of machine learning and mathematical research, demonstrating the potential of ML-based regression models in number theory and complex sequence analysis.