This paper presents a more unified form for overall scalable and intelligent processing of data that delivers artificial intelligence, big data analytics, data engineering pipelines, and cloud-native architectures together. The issue seen in the analysis of enabling large-scale heterogeneous data, is that it combines sophisticated artificial intelligence algorithms and efficiently implementing data engineering workflows that leverage distributed cloud-native operational capacity. We explored the performance of four algorithms, Gradient Boosted Trees, Deep Neural Networks, K-Means Clustering, and Apache Spark Streaming on a integrated data processing and analysis system. The findings from the experiment show an overall data processing throughput increase of 35%, reduction in latency by 28%, and predictive accuracy improvement of 22% relative to benchmarked model performances. Furthermore, cloud-native deployments promote overall system scalability by improving elasticity and resource allocation, which had a corresponding savings of approximately 30% in overall operating costs. In then comparing to current existing solutions, the integrated framework improved implementation flexibility for both batch and streaming data while providing the highest levels of reliability and fault tolerance. This unified framework presents us with a robust and flexible solution to offering vastly different applications while integrating with healthcare, manufacturing and finance. Future research will emphasize the exploration of further flexibility and self-learning into the framework.