In the following article, we apply a set of machine learning algorithms to analyse a set of 445 Californian Hospitals. We investigate a variable that can be considered as a synthesis of the economic, financial and organizational performance of the hospital i.e. Net Income. First of all, we have applied a regression analysis with OLS-Ordinary Least Squares to verify the presence of significant relationships among the variables in respect to Net Income. Furthermore, we have applied the k-Means algorithm optimized with the Elbow Method to verify the presence of groups of hospitals in the dataset based on more than 200 variables and centred on Net Income. Finally, we propose a comparison among eight different machine-learning algorithms to estimate the future value of Net Income based on an historical series in the period 2014-2018. Our idea is that the area of inefficiency that are showed thanks to the regression analysis can be optimized with the application of AI and Lean Management. Specifically, the efficiency of hospitals to manage human resources and specifically physicians can be improved with the application of telemedicine and organizational tools, that can increase either the performance of the hospital and the level of care offered to patients. The mix of Artificial Intelligence and Lean Management can promote better models in healthcare, reducing costs, improving the quality of services, increasing the level of human resources especially physicians, to create a more sustainable and reliable healthcare system.