Predictive Integration Analytics A Machine Learning Framework For Api Performance Optimization And Anomaly Detection

Abstract

This study presents an AI-driven framework for predictive API performance monitoring, integration reliability, and anomaly detection. The proposed approach combines machine learning techniques, including PyCaret, LightGBM, XGBoost, Isolation Forest, and Autoencoders, to improve the reliability of API-dependent systems. The framework reduces Mean Time to Resolution (MTTR) by 40%, decreases false-positive alerts by 25%, and identifies potential anomalies approximately 30 minutes before their operational impact. Unlike conventional monitoring approaches that primarily focus on infrastructure-level indicators, the proposed framework analyzes request- and endpoint-level telemetry, including latency, payload size, HTTP status-code distributions, authentication events, and API call-sequence patterns. This enables real-time identification of abnormal behavior and supports intelligent auto-scaling, API abuse detection, and early failure identification across internal microservices and third-party integration dependencies. The framework was evaluated across finance, healthcare, and retail-oriented scenarios, demonstrating a 20% reduction in operational costs and improved resilience during peak workloads and integration-partner failures. Automated CI/CD integration, adaptive model retraining, and AI-assisted root-cause analysis further enhance the framework's ability to respond to changing workloads. The proposed approach therefore provides a scalable and adaptive mechanism for predictive API performance optimization and reliable integration management in modern cloud environments.