Streaming pipelines need to be able to handle datasets that change quickly and have schemas that change as well in today's fast-paced, data-driven world. When schema changes happen that aren't expected, traditional schema management methods that rely on static rules or manual updates typically don't work, which might break pipelines or corrupt data. To solve this problem, we provide AutoSchema, a self-supervised learning framework that can find and fix schema drift in real-time data streams without any help from people. AutoSchema employs a two-part neural approach: A drift detection system that finds schema problems via contrastive learning. This cuts down on false alarms by a lot compared to older rule-based systems. A dynamic schema adapter that uses graph-based metadata learning to rebuild and check new schema mappings as they happen, preventing pipeline problems. We used AutoSchema on a variety of streaming datasets, such as IoT sensors, financial transactions, and log analytics, and found that it was 98.3% accurate in finding schema drift (22% better than rule-based methods). Adaptation in less than a second, which keeps the data flowing. 70% faster recovery than the best tools for schema evolution. Our results show that AutoSchema makes pipelines more resilient to schema changes while keeping costs low, making it perfect for big applications. This study fills in a big gap in autonomous data management by giving businesses a dependable way to handle dynamic streaming settings.