Machine learning algorithms are revolutionizing intelligent search and information discovery capabilities. By incorporating techniques like supervised learning, unsupervised learning, reinforcement learning, and deep learning, systems can automatically extract insights and patterns from vast data repositories. Natural language processing enables deeper comprehension of text, while image recognition unlocks knowledge from visual data. Machine learning powers personalized recommendation engines and accurate sentiment analysis. Integrating knowledge graphs enriches machine learning models with background knowledge for enhanced accuracy and explainability. Applications span voice search, anomaly detection, predictive analytics, text mining, and data clustering. However, interpretable AI models are crucial for enabling transparency and trustworthiness. Key challenges include limited training data, complex domain knowledge requirements, and ethical considerations around bias and privacy. Ongoing research that combines machine learning, knowledge representation, and human-centered design will advance intelligent search and discovery. The collaboration between artificial and human intelligence holds the potential to revolutionize information access and knowledge acquisition.