Transfer learning is a rapidly growing research area in machine learning that focuses on leveraging knowledge gained from one domain or task to improve learning performance in a related but different domain or task. Research papers in this domain explore applications across computer vision, natural language processing, speech recognition, healthcare, IoT, and finance. Key techniques include fine-tuning pretrained models, domain adaptation, multi-task learning, and few-shot or zero-shot learning. Recent studies investigate integrating transfer learning with deep neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and graph neural networks to improve efficiency, accuracy, and generalization. Challenges addressed in the literature include domain shift, negative transfer, limited labeled data, computational efficiency, and interpretability. By leveraging transfer learning, research aims to reduce training time, improve model performance in low-resource scenarios, and enable adaptive, scalable solutions across diverse real-world applications.