This paper presents a study between Leela Chess Zero (Lc0), a neural network-based chess engine and elite-level human chess play. The objective of this research is to explore how artificial intelligence, reinforcement learning and neural network-based systems, are reshaping our understanding of chess. We have extracted and modified large datasets from public domain comprising over 2.7 million self-play games by Lc0 and 1.6 million games played by elite human players (rated above 2500 on Lichens platform) and pre-processed this data to enable robust statistical analyses. With the help of a systematic approach and Python-based data analysis, we have evaluated various dimensions of game play under different conditions. This study investigates not only the strengths of neural network-based engines in decision-making and pattern recognition but also highlights the blind spots and biases in elite human play of opening selection and game outcomes. Charts and visualizations have been shown to clearly represent key differences and correlations between the two types of play. Our findings suggest that AI engines like Lc0 do not just replicate human strategies but often uncover new approaches and deeper evaluations that challenge long human theories. The analysis shows how neural network-based engines expose flaws in classical human openings and they often suggest alternative continuations that were previously unexplored. Also, we discuss the role of tree search in enhancing prediction accuracy, especially in critical game phases such as endgames. This study aims to provide a bridge between human level game understanding and AI precision in chess, offering insights that can help chess professionals to enhance their play and rethink on traditional strategies. The results show how AI can be used as a powerful tool for chess analysis, training, and innovation. In the long term, this research can influence how chess is taught, analysed, and played, leading to a reEvaluation of classical openings and the discovery of new theoretical lines.