In this paper, we introduce an Automated and Scalable Hybrid Continual Learning Framework (ASHCLF), a new methodology specifically designed to address the limitations above in terms of high catastrophic forgetting rates as well as the ability for scalability and cross-domain adaptability within continual learning systems. Online learning is important for incremental continual learning, where machine models can learn new tasks sequentially and remember the previous ones. Nonetheless, state-of-the-art approaches such as Elastic Weight Consolidation (EWC) and Learning without Forgetting (LwF) are not optimized to deal with large-scale data or task diversity in practice and often involve extensive manual tuning. ASHCLF is innovative in that, unlike classical methods, which use hand-tuned algorithms and architectures for each separate language pair model of machine translation, the dynamic architecture, hyper-parameter tuning automatically, and cross-domain adaptation presented allow it to change its complexity according to task requirements. A thorough evaluation of several common domain-generalization benchmarks (AG News, CIFAR-10) and a financial time-series forecasting dataset justifies the generalizability of this framework across diverse data modalities such as text, images, and temporal domains. The experimental results show that the ASHCLF framework consistently outperforms established methods, achieving a notable 89.5% in accuracy retention while reducing the forgetting rate to just 8.3%. Additionally, it demonstrates superior cross-domain generalization, particularly in challenging scenarios involving diverse data modalities.