Continual Learning Frameworks for Long-Term Knowledge Retention in Evolving Data Streams
Machine learning systems are commonly developed under the assumption of static data distributions, limiting their effectiveness in real-world environments where data evolve continuously. In dynamic domains such as cybersecurity monitoring, financial analytics, and intelligent recommendation systems, models must adapt to new information while preserving previously acquired knowledge. However, sequential learning often leads to catastrophic forgetting, where newly learned information overwrites earlier knowledge representations. Existing continual learning approaches partially address this issue but frequently rely on large memory buffers, explicit task boundaries, or computationally expensive retraining strategies, which limit their scalability in real-world streaming environments. To address this gap, this study proposes a modular continual learning framework that integrates incremental model updates with a memory-based rehearsal mechanism designed to preserve representative samples from previously learned tasks. The framework enables models to adapt to evolving data streams while maintaining knowledge retention with minimal memory overhead. Experiments were conducted using sequential learning benchmarks that simulate realistic data evolution scenarios with varying levels of task similarity and data drift. The results demonstrate that the proposed approach maintains stable predictive performance across tasks while significantly reducing catastrophic forgetting compared with conventional sequential training strategies. In particular, the framework achieves consistent task accuracy with substantially lower forgetting rates and reduced retraining cost, highlighting its effectiveness for long-term adaptive learning. The findings suggest that combining incremental learning with compact rehearsal memory provides a practical solution for building adaptive AI systems capable of sustained learning in evolving data environments.