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Abstract: The paper provides an overview of the development and intelligent voice data analysis from a machine learning perspective; a historical, state-of-the-art view and a view on some future trends in the field of artificial intelligence. The paper describes some areas within voice recognition domain which seem to be important for applying machine learning in medical diagnosis. This describes a recently developed method of detecting respiratory problems quickly by recognizing the changes in voice over time. Machine learning algorithms are applied here. Machine Learning is the core subarea of artificial intelligence. The different techniques available for Machine Learning are Linear Regression, Logistic Regression, Decision Tree, SVM, Naive Bayes, k-NN, K-Means, Random Forest, Dimensionality Reduction Algorithms and Gradient Boosting algorithms. The main idea of the paper is to apply Logistic Regression, K-nearest neighbors, Support Vector Machine (SVM), Naïve Bayes and Random Forest Algorithms in recognition and detection of respiratory problems based on voice. The findings of this paper contribute to the healthcare system.