Strawberry (Fragaria × ananassa Duch.) is a highly valuable crop worldwide, with significant market demand. These plants are highly susceptible to various diseases, such as powdery mildew, leaf scorch, gray mold, and anthracnose, which can significantly impact fruit yield and quality. Early detection and classification of these diseases are essential for effective disease management in agriculture. Traditional manual inspection methods are slow, labor-intensive, and prone to human error, which can lead to delayed interventions and increased crop loss. Machine learning, particularly deep learning, has emerged as a powerful tool for automating the detection of strawberry plant diseases, offering higher accuracy and faster results. This paper reviews key machine learning models used for strawberry disease detection, including Convolutional Neural Networks (CNNs), transfer learning, Deep Metric Learning (DML), Instance Segmentation models, and Recurrent Neural Networks (RNNs). The review highlights the performance, limitations, and future potential of these models, with a focus on real-world applications.