The rapid proliferation of digital technologies has led to an unprecedented growth in data generation across industries, organizations, and digital ecosystems. Managing, processing, and extracting meaningful insights from massive volumes of heterogeneous data has become one of the defining technological challenges of the contemporary era. Big data analytics has emerged as a powerful paradigm capable of transforming raw data into actionable intelligence, enabling organizations to make informed decisions and achieve operational efficiency. However, traditional computing infrastructures often struggle to handle the volume, velocity, and variety associated with modern datasets. Cloud computing has consequently emerged as a complementary technological framework that provides scalable, flexible, and cost-efficient computational resources for big data processing. This study presents a comprehensive examination of the integration of big data analytics with cloud computing infrastructures, exploring architectural models, technological frameworks, security considerations, and domain-specific applications. Drawing upon established academic literature, the research analyzes how cloud-enabled platforms facilitate scalable analytics through distributed processing systems, elastic storage capabilities, and advanced machine learning integration. The study further investigates sectoral implementations across healthcare, finance, e-commerce, smart cities, and disaster management systems, highlighting the transformative potential of cloud-based analytics in enhancing operational efficiency and decision-making processes. Particular emphasis is placed on the security challenges associated with storing and processing large-scale datasets in cloud environments, including issues related to privacy, data governance, and cyber vulnerabilities. Through a detailed theoretical and analytical exploration, this research demonstrates that cloud computing not only resolves the computational limitations of traditional big data infrastructures but also introduces new paradigms for collaborative analytics, real-time processing, and intelligent automation. Nevertheless, the increasing reliance on cloud platforms necessitates robust security frameworks, governance models, and regulatory compliance mechanisms to safeguard sensitive information and ensure system resilience. The study concludes by outlining future research directions in areas such as hybrid cloud architectures, AI-driven analytics pipelines, and decentralized data governance models, which are likely to shape the next generation of cloud-enabled big data ecosystems.