The current data validation systems are mostly reactive, static, and resource-heavy which may lead to interruptions of pipelines and will not be able to detect data corruption in real-time settings. This paper presents SYNAPSE-X, an AI-based, zero-downtime system to perform predictive, adaptive, and self-healing data validation in intelligent data pipelines to overcome these shortcomings. The framework presents some of the main innovations, such as Validation DNA, a single representation of data quality features; the Spatio-Temporal Validation Graph (STVG) of context-aware dependency modeling and trust propagation; an Adaptive Validation Controller (AVC++) based on reinforcement learning, to make dynamic decisions; and the Self-Healing Reconstruction Engine (SHRE) to recover data autonomously. Experimental testing shows that there is a substantial performance improvement, such as better validation accuracy, less computational cost, virtually zero downtime, and recovery in the face of corrupted data situations, than in traditional validation methods. The key contributions of this work are: (i) a new predictive validation architecture, (ii) combined graph-based validation system, (iii) adaptive optimization process based on RL, and (iv) autonomous self-healing data validation system.