In a constantly changing world, AI models require a Continual Learning (CL) approach to adapt effectively. Imagine a garden robot trained to water plants based on data collected during one season. As the garden changes with blooming flowers, the robot fails to recognize the new environment and struggles to perform its tasks. Retraining the model from scratch is costly and impractical, especially without historical data. Fine-tuning the model with new samples risks catastrophic forgetting, where previously learned skills are lost. Continual Learning offers an alternative by balancing model stability (retaining old information) and plasticity (adapting to new data). CL methods include regularization-based approaches that add terms to the loss function to balance old and new tasks, replay-based approaches that use historical data to mitigate forgetting, optimization-based approaches that adjust optimization methods to preserve performance across tasks, representation-based approaches that develop robust feature representations, and architecture-based approaches that allocate new task-specific subspaces in the network. Evaluating CL models involves assessing overall performance, memory stability, and learning plasticity. Despite its advantages, CL is not yet universally adopted due to limited interpretability, synthetic benchmarks that don't reflect real-world scenarios, and a focus on storage over computational costs. However, CL addresses the significant challenge of changing data distributions, offering economic and environmental benefits by reducing the need for extensive retraining. CL methods are beneficial for various applications, such as model editing, personalizing models for specific users, on-device learning with limited resources, faster retraining with minimal updates, and reinforcement learning in non-stationary environments. Improving CL methods can make AI models more accessible, sustainable, and versatile, promoting broader adoption and better performance in dynamic settings.