A Modern Approach To Computational Complexity Solution to P vs NP Abstract Computer Science is a discipline that encompasses the study of computation, information, and automation. Its primary objective is to uncover patterns in natural processes and discover rules or laws governing equilibrium. While the seminal papers by Church and Turing in the 1930s laid the groundwork for this science, they are no longer sufficient as fundamental theories. Over the past century, significant developments have emerged. Group theory, which had been in existence for a century, underwent further advancements. Shannon introduced a mathematical theory of communication, while Jay W. Forrester and Donella Meadows pioneered the field of systems thinking. Quantum computing has also gained significant momentum in recent decades. This paper delves into crucial aspects of the Turing Machine, exploring its definitions, as well as providing a deeper understanding of systems and machines. As a result, a novel approach to computational complexity is presented, offering a proof of the P vs NP problem. The constructive strategy employed opens up a more realistic pathway for problem-solving and computational complexity analysis. To illustrate these concepts in practice, an implementation is provided as an example of a distributed system, where decentralization, scalability and self tuning are among the most important features.