Machine learning algorithms evolve over time through processes such as training, validation, and retraining. As they analyze new data, these algorithms adapt their models to improve accuracy and performance. Continuous learning enables them to recognize patterns and make predictions based on the most relevant information. Techniques like supervised and unsupervised learning drive this evolution, allowing algorithms to refine their outputs over time. As the quality and quantity of data grow, machine learning algorithms become increasingly capable, enhancing their applications across various industries.