The aging global population presents a significant challenge to healthcare systems, necessitating the early identification and management of age-related conditions. This chapter introduces an innovative method employing machine learning techniques for the identification of age-related conditions through the analysis of anonymous characteristic measurements. The data collection process involves gathering measurements of anonymous characteristics, such as gait patterns, voice recordings, and typing behavior, which are relevant to age-related conditions. The model is trained and validated using a curated dataset collected from a diverse group of elderly individuals, ensuring the representativeness and generalizability of the predictions. To evaluate the performance of the model, various performance metrics, including accuracy, sensitivity, specificity, and overall performance, are employed in detecting age-related conditions. Additionally, a comparison is made with traditional diagnostic methods to highlight the advantages of utilizing anonymous measurements. To maintain participant privacy and adhere to ethical guidelines, extensive measures are implemented to anonymize the data effectively. By ensuring data privacy through the anonymization of personal information, our approach provides valuable insights while upholding ethical considerations.