Dynamic data distributions, system failures, and low-latency, cost-effective processing are becoming more of a challenge to modern real-time data pipelines. Current streaming architectures are based on relatively static settings and reactive processes and are thus not capable of responding to changing conditions and ensuring consistent performance. In this paper, a smart and responsive framework that combines real-time validation, predictive anomaly detection and autonomous recovery, and learning-based optimization is proposed in one pipeline architecture. The suggested solution allows to maintain constant monitoring and decision making with the help of the closed-loop system balancing dynamically the latency, computational cost, and the quality of the data. An integrated verification system maximizes the effectiveness of anomaly detection and an independent recovery system guarantees the quick recovery of faults and the recovery of the system with the lowest downtimes. Experimental assessment of various situations proves to be dramatically better than traditional systems with lower latency diversity, shorter recovery period, greater detection precision, and more economical in limited situations. The findings indicate the possibility of turning the conventional reactive pipelines into proactive and self-adaptive systems, which provides a scalable and robust solution to the current data-intensive applications like financial analytics, IoT monitoring, and large-scale cloud environments.