This survey paper provides a comprehensive overview of transfer learning, a methodology that leverages information from related domains to improve performance on a target domain. The paper begins by defining transfer learning and highlighting its importance in scenarios where training data is expensive or difficult to collect. It then reviews current solutions and applications of transfer learning, emphasizing its relevance in various domains such as text sentiment classification, image classification, and human activity recognition. The paper also discusses the challenges of domain adaptation, including distribution differences between source and target domains, and introduces the concept of negative transfer, where information from the source domain negatively impacts the target learner. The survey is organized into sections covering homogeneous and heterogeneous transfer learning, negative transfer, and specific applications. It details various strategies for addressing distribution differences, such as instance-based, feature-based, parameter-based, and relational-based approaches. Each section includes a discussion of specific algorithms and their performance in different applications, along with experimental results and performance metrics. The paper concludes with a discussion on future research directions and a list of software downloads for various transfer learning solutions. It emphasizes the applicability of transfer learning to big data environments and highlights the need for further research to address the limitations and enhance the effectiveness of transfer learning techniques.This survey paper provides a comprehensive overview of transfer learning, a methodology that leverages information from related domains to improve performance on a target domain. The paper begins by defining transfer learning and highlighting its importance in scenarios where training data is expensive or difficult to collect. It then reviews current solutions and applications of transfer learning, emphasizing its relevance in various domains such as text sentiment classification, image classification, and human activity recognition. The paper also discusses the challenges of domain adaptation, including distribution differences between source and target domains, and introduces the concept of negative transfer, where information from the source domain negatively impacts the target learner. The survey is organized into sections covering homogeneous and heterogeneous transfer learning, negative transfer, and specific applications. It details various strategies for addressing distribution differences, such as instance-based, feature-based, parameter-based, and relational-based approaches. Each section includes a discussion of specific algorithms and their performance in different applications, along with experimental results and performance metrics. The paper concludes with a discussion on future research directions and a list of software downloads for various transfer learning solutions. It emphasizes the applicability of transfer learning to big data environments and highlights the need for further research to address the limitations and enhance the effectiveness of transfer learning techniques.