An engineering application project is being presented, in which various machine learning techniques have been applied. These techniques involve the use of classification algorithms and data analysis techniques. Methods were designed using the Python programming language and available libraries for data analysis and machine learning. The purpose of this technological solution is to offer a first-level support alternative in the Mexican healthcare system for the diagnosis of sarcopenia in older adults. Sarcopenia currently has known diagnoses by physicians, but some of these methods are costly, and not all hospitals have access to them. However, there is a possibility of finding certain secondary variables that are related to the disease and can be classified in a software tool that helps primary care physicians recognize risk characteristics in patients in a timely manner. This would ensure that patients who may have sarcopenia receive appropriate attention and medication. The data used in this project comes from the General Hospital of Tijuana, where we have anonymized data from 100 individuals.