The exponential growth of data-intensive applications has transformed the landscape of distributed systems, necessitating architectures that integrate real-time data ingestion, scalable computation, intelligent analytics, and adaptive resource management. This research synthesizes foundational and contemporary contributions in distributed messaging systems, MapReduce-based file management, serverless computing, complex event processing, resource-aware partitioning, approximate nearest neighbor search, and machine learning frameworks to propose a unified architectural paradigm for intelligent real-time distributed systems. Drawing upon seminal works on Apache Kafka (Kreps, Narkhede, & Rao, 2011), adaptive MapReduce storage (Tudoran, Costan, & Antoniu, 2014), MLlib in Apache Spark (Meng et al., 2016), serverless computing (Grier, 2019), complex event processing (Cugola & Margara, 2012), resource-aware partitioning (Kulkarni et al., 2015), and approximate nearest neighbor algorithms (Arya & Mount, 1993; Kleinberg, 1997; Indyk & Motwani, 1998; Kushilevitz, Ostrovsky, & Rabani, 1998), the study develops a comprehensive theoretical framework for scalable intelligent infrastructures. Furthermore, emerging AI-powered neuroprosthetic systems (Pulicharla & Premani, 2024) are examined as a high-impact application domain requiring ultra-low-latency analytics and adaptive distributed coordination. The study proposes an integrative architecture that leverages event streaming, distributed machine learning, approximate similarity search, and serverless orchestration while incorporating adaptive leader selection mechanisms for reliability (Sayyed, 2025). Through detailed conceptual modeling and scenario-based evaluation, the findings demonstrate that synergistic integration of these paradigms enhances scalability, resilience, responsiveness, and computational efficiency. The paper concludes by identifying research frontiers in hybrid cloud optimization, edge deployment, and cognitive cyber-physical systems.