The rapid evolution of Artificial Intelligence (AI) systems has been significantly accelerated by the proliferation of Big Data, enabling more accurate, scalable, and adaptive computational models. However, integrating Big Data with AI systems introduces critical challenges related to data heterogeneity, computational scalability, privacy constraints, and system optimization. This paper proposes an advanced analytical architecture designed to efficiently leverage Big Data for AI-driven systems through structured data pipelines, optimization strategies, and case-based evaluation frameworks. The study synthesizes foundational theories in machine learning, distributed computing, and data analytics while critically examining architectural models such as MapReduce-based frameworks, edge-cloud hybrid systems, and machine learning pipelines. Drawing upon established literature, including healthcare analytics, Industry 4.0 systems, and privacy-preserving data models, the proposed framework integrates multi-layered data processing, adaptive optimization mechanisms, and AI model orchestration techniques. The research also highlights the socio-technical implications of Big Data systems, emphasizing ethical concerns, interpretability, and scalability constraints as emphasized in prior scholarly discourse (Boyd and Crawford, 2012). Findings suggest that a unified analytical architecture improves computational efficiency, reduces latency in decision-making, and enhances predictive accuracy across domains such as healthcare, smart systems, and financial analytics. The paper contributes a structured conceptual model for researchers and practitioners aiming to design next-generation AI systems powered by Big Data ecosystems.