Continual Learning is a modern approach in machine learning that ensures models remain accurate and relevant by continuously updating them with new data, rather than retraining from scratch. This process is crucial in environments with dynamic data, evolving domains, and user-personalized systems such as social media, finance, and recommendation engines. Continual Learning involves a recurring cycle of collecting, fine-tuning, evaluating, deploying, and monitoring models. It offers a strategic advantage by adapting AI systems to real-world changes, addressing issues like data drift and model decay. Techniques such as instruction tuning, task-specific fine-tuning, and preference alignment are employed to refine models, while hybrid strategies combining Continual Learning and full retraining are often used for comprehensive updates. The approach also tackles challenges like catastrophic forgetting through methods like replaying old data and using regularization. Continual Learning is not just a technical enhancement but a strategic capability that transforms AI from static artifacts into adaptive systems, maintaining performance and relevance in the face of constant change.