Image recognition is one of the most advanced and impactful fields within Artificial Intelligence, with applications ranging from medical diagnostics to industrial monitoring systems. However, training deep learning models from scratch requires massive datasets and substantial computational resources, which are not always available in research environments. Transfer Learning addresses this limitation by reusing pre-trained models developed on large-scale datasets and adapting them to specific tasks. This is typically achieved by freezing lower layers responsible for general feature extraction and retraining higher layers to suit the new classification objective. This approach significantly reduces training time and computational cost while minimizing the risk of overfitting. Research indicates that combining transfer learning with data augmentation techniques further enhances model robustness and accuracy. Moreover, transfer learning enables efficient development of solutions in sensitive domains such as early tumor detection, satellite image analysis, and autonomous driving systems. Nevertheless, careful selection of the base model is essential to ensure compatibility with the new dataset characteristics. Thus, transfer learning represents a strategic methodology for building high-performance image recognition systems with optimized efficiency.