The field of continual learning is witnessing significant advancements aimed at addressing the challenges of catastrophic forgetting and efficient knowledge retention. Recent developments emphasize innovative strategies that balance the stability-plasticity dilemma, enhance knowledge transfer, and optimize computational efficiency. Key approaches include leveraging pre-trained models with task-specific adapters, adaptive prompt learning, and novel architectural strategies that exploit model sparsity and weight sharing. These methods are designed to mitigate catastrophic forgetting, improve model capacity utilization, and reduce computational costs. Notably, advancements in semantic segmentation and class-incremental learning showcase the integration of adaptive prototypes, uncertainty-aware constraints, and balanced gradient sample retrieval to enhance knowledge retention and task performance. Overall, the field is progressing towards more efficient, adaptable, and robust continual learning frameworks.