Machine learning in COVID-19 diagnosis is an active research area that leverages computational models to assist in the rapid, accurate, and scalable detection of SARS-CoV-2 infections using clinical, imaging, and molecular data. Traditional diagnostic methods like RT-PCR and serological testing face limitations in speed, accessibility, and sensitivity, whereas machine learning models can analyze complex datasets for early detection and risk assessment. Early studies employed classical machine learning algorithms such as support vector machines (SVM), random forests, and logistic regression on clinical and laboratory data. Recent research focuses on deep learning approaches, including convolutional neural networks (CNNs) for chest X-ray and CT scan analysis, recurrent neural networks (RNNs) and transformers for temporal clinical data, and hybrid architectures for multi-modal fusion of imaging, genomic, and epidemiological data. Applications include automated detection, severity assessment, prognosis prediction, and monitoring of disease progression. Current studies also explore data augmentation, transfer learning, explainable AI for clinical interpretability, and federated learning to preserve privacy across hospitals, establishing machine learning as a critical enabler for accurate, efficient, and scalable COVID-19 diagnosis and healthcare decision support.