The increasing prevalence of time-series data in various domains necessitates efficient tools for real-time analysis and anomaly detection. Existing solutions often face challenges with scalability, usability, or domain-specific integration. To address these issues, we introduce STREAM, a modular framework that integrates data ingestion, ML model training, anomaly detection, visualization, and alert management within a user-friendly browser interface. STREAM utilizes advanced AI, including transformer models and expert systems, to ensure robust and interpretable monitoring. Key features—such as multi-user support, Docker-based scalability, metadata enrichment, and a searchable model repository—enable seamless data analysis and automation without programming expertise. STREAM’s real-time capabilities make it a valuable resource for both academia and industry, streamlining AI workflows and delivering actionable insights.