This paper conducts a systematic literature review (SLR) to investigate the application of Artificial Intelligence (AI) techniques in the identification and classification of software requirements. The primary objective is to understand how AI techniques, including Machine Learning (ML), Deep Learning (DL), and Transfer Learning (TL), can support requirements engineering (RE). The review covers 61 studies published between 2012 and 2022, focusing on the use of AI techniques in requirements classification from project documents and app reviews. Key findings include: 1. **Transfer Learning-Based Approaches**: These approaches are extensively used and yield the most accurate results, outperforming other ML and DL techniques. 2. **Common Techniques**: Support Vector Machine (SVM) and Convolutional Neural Network (CNN) are widely used in selected studies. 3. **Evaluation Metrics**: Precision and Recall are commonly used to evaluate the performance of automated techniques. 4. **Challenges**: While AI approaches show promising results, their applicability in complex and real-world settings remains underexplored. 5. **Future Directions**: The study calls for a closer collaboration between RE and AI techniques to address open issues in automated system development. The review also discusses the limitations of existing studies, such as the use of old datasets and the lack of detailed explanations for evaluation metrics. Open challenges include the need for new benchmark datasets and the optimization of computational costs. The paper concludes by highlighting the potential of AI techniques in enhancing RE tasks and software engineering processes.This paper conducts a systematic literature review (SLR) to investigate the application of Artificial Intelligence (AI) techniques in the identification and classification of software requirements. The primary objective is to understand how AI techniques, including Machine Learning (ML), Deep Learning (DL), and Transfer Learning (TL), can support requirements engineering (RE). The review covers 61 studies published between 2012 and 2022, focusing on the use of AI techniques in requirements classification from project documents and app reviews. Key findings include: 1. **Transfer Learning-Based Approaches**: These approaches are extensively used and yield the most accurate results, outperforming other ML and DL techniques. 2. **Common Techniques**: Support Vector Machine (SVM) and Convolutional Neural Network (CNN) are widely used in selected studies. 3. **Evaluation Metrics**: Precision and Recall are commonly used to evaluate the performance of automated techniques. 4. **Challenges**: While AI approaches show promising results, their applicability in complex and real-world settings remains underexplored. 5. **Future Directions**: The study calls for a closer collaboration between RE and AI techniques to address open issues in automated system development. The review also discusses the limitations of existing studies, such as the use of old datasets and the lack of detailed explanations for evaluation metrics. Open challenges include the need for new benchmark datasets and the optimization of computational costs. The paper concludes by highlighting the potential of AI techniques in enhancing RE tasks and software engineering processes.