This systematic review synthesizes theoretical perspectives comparing artificial intelligence (AI) and human cognition, focusing on how intelligence, consciousness, and rationality intersect. Its primary aim is to clarify the conceptual boundaries and points of convergence between machine and human minds as discussed in contemporary scholarship. Guided by Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 standards, the review analyzed 10 peer-reviewed theoretical and analytical papers published between 2015 and 2025, retrieved from six major databases, including IEEE Xplore, Scopus, and ScienceDirect. Eligible studies were thematically coded to identify prevailing frameworks and citation patterns. Three key findings emerged from the synthesis. First, the global workspace theory continues to dominate theoretical discussions on consciousness, serving as a central model for explaining cognitive integration. Second, computational rationality appears to be the most frequently applied framework, providing a common ground for understanding decision-making in both cognitive and artificial systems. Third, the emerging notion of human–machine duality marks a paradigmatic shift from comparative analysis toward integrative theorization. Overall, the review reveals that despite advances in computational modeling, a persistent “hard problem” gap remains between functional and phenomenal consciousness. AI has rapidly evolved from rule-based systems to sophisticated learning architectures capable of pattern recognition, problem-solving, and limited reasoning. Consolidating existing frameworks, this study contributes to bridging philosophical inquiry with computational theory in advancing future models of intelligent systems.