Browser fingerprinting has emerged as a critical technique for device identification, fraud detection, behavioral analytics, and cybersecurity monitoring. However, the increasing availability of anti-fingerprinting tools, browser spoofing frameworks, privacy-enhancing extensions, and adversarial machine learning techniques has significantly reduced the reliability of conventional browser fingerprinting systems. Under adversarial conditions, attackers manipulate browser attributes, inject synthetic behaviors, and employ evasion mechanisms to bypass detection models. This paper proposes an AI-driven detection and defense architecture designed to enhance the robustness of browser fingerprinting against adversarial manipulation. The study synthesizes concepts from artificial intelligence, edge intelligence, decentralized computing, data science, and complex systems engineering to develop a multi-layer adaptive framework capable of detecting anomalous fingerprint behaviors and adversarial perturbations. The architecture integrates feature integrity analysis, adversarial anomaly detection, behavioral correlation learning, decentralized verification mechanisms, and edge-based intelligence processing. Findings indicate that AI-enabled adaptive fingerprinting provides improved resilience against spoofing attacks, fingerprint randomization, and identity obfuscation techniques. The proposed framework contributes to the development of trustworthy browser identification systems capable of operating effectively in increasingly hostile digital environments.