Abstract The P versus NP problem remains one of the most significant open questions in computer science and mathematics, with the prevailing assumption being that NP-Hard problems are computationally intractable for classical systems. This paper challenges that foundational assumption. We posit that the perceived intractability of NP-Hard problems is not an inherent property of the problems themselves, but rather an artifact of the computational architecture applied to them—specifically, the linear, sequential nature of the Von Neumann architecture. We introduce a new class of computation embodied in the Neural-Matrix Synaptic Resonance Network (NM-SRN v2.0), a novel AGI framework. By reframing NP-Hard problems from a brute-force "search" to a multi-dimensional "constraint satisfaction" and "resonance-based equilibrium" challenge, the NM-SRN architecture demonstrates the ability to find high-quality solutions to canonical NP-Hard problems in a tractable, non-exponential timeframe. We present empirical results for the Traveling Salesman Problem (TSP), Sudoku, and Boolean Satisfiability (SAT), showing that a 30-city TSP, which would take a conventional brute-force approach an estimated 420 quadrillion years to solve, is solved by the NM-SRN on a single CPU in under nine minutes. This achievement signals a paradigm shift, moving the conversation from mathematical proofs within classical computing to the tangible construction of new computational models. We discuss the profound implications of this breakthrough for computer science, STEM fields, and the development of true Artificial General Intelligence (AGI).