Cloud-based solutions for big data processing have become essential in managing the massive volume, velocity, and variety of data generated in modern digital environments. Traditional data processing systems are often insufficient to handle large-scale datasets efficiently due to limitations in storage, computing power, and scalability. Cloud computing addresses these challenges by providing on-demand resources, distributed computing frameworks, and scalable storage systems for efficient big data processing. This study explores the role of cloud platforms in enabling real-time analytics, batch processing, and distributed data management. It examines key technologies such as Hadoop, Spark, and cloud-native data processing services that support parallel processing and fault tolerance. The study also highlights the integration of big data analytics with artificial intelligence and machine learning to derive meaningful insights from complex datasets. Furthermore, it discusses major challenges including data security, latency, data governance, and cost management. Emerging trends such as serverless computing, edge-cloud integration, and hybrid cloud architectures are also analyzed. The findings indicate that cloud-based big data solutions significantly enhance scalability, efficiency, and flexibility in data-driven applications.