A Novel 3D Predictive Model for Prime Numbers

Prime numbers are fundamental to mathematics, yet their distribution remains one of the greatest unsolved problems. Traditional research has focused on analytical approaches such as the Riemann Hypothesis, but no simple function has been able to deterministically predict primes. This paper introduces a novel approach to understanding prime distributions by analyzing prime number triplets in a three-dimensional coordinate system. I employ statistical modeling, machine learning, and regression techniques to derive highly accurate predictive equations for prime numbers. My findings suggest an unexpectedly strong relationship between prime triplets, challenging the conventional notion of prime randomness. This work opens new doors for prime number research and its applications in cryptography and computational number theory.