In today’s data-driven fintech landscape, managing the end-to-end product lifecycle requires seamless integration of structured and unstructured data from multiple sources, including customer feedback systems, regulatory inputs, engineering pipelines, and business analytics dashboards. Traditional integration and development approaches often struggle to adapt to the velocity and volume of this data, resulting in fragmented workflows and inconsistent feature delivery. This paper presents an integrated architecture that leverages Amazon Web Services (AWS) to orchestrate data ingestion, transformation, and machine learning (ML)-driven prioritization for optimized product lifecycle management. Using key AWS services such as AWS Glue, Lambda, S3, SageMaker, and Step Functions, the architecture supports real-time data syncing across cross-functional teams. These tools enable dynamic extraction and loading of product metrics, automated preprocessing, and on-demand model inference. The machine learning component centers on Support Vector Machine (SVM) classifiers to prioritize product backlog features based on multidimensional inputs such as feature usage trends, customer sentiment, technical complexity, and compliance urgency. The outputs feed directly into Agile development workflows using CI/CD integration and are aligned with Six Sigma quality controls to monitor delivery accuracy. The framework also incorporates DevOps practices to ensure operational resilience, cost efficiency, and model governance. Agile principles guide the sprint planning and deployment phases, while Six Sigma metrics such as defect rates and DPMO provide structured feedback loops. Through this synergistic model, the study demonstrates how AWS-native infrastructure can be harnessed to support scalable, transparent, and intelligent fintech product development. This paper contributes a repeatable blueprint for organizations aiming to transform fragmented development pipelines into an integrated, ML-powered decision ecosystem.