Monitoring biomarkers is essential for patients to effectively manage their health profiles and prevent potential complications. Regular tracking of these indicators allows for timely interventions and better control over one’s health, particularly when the methods employed are non-invasive and grant convenience and comfort to the patient. Conventionally, this monitoring is accomplished in pathology laboratories, by collecting blood samples or finger-pricking, which can be distressing and impractical for regular use. Given the growing need for more accessible and affordable healthcare solutions, the development of a cost-effective non-invasive method has become crucial. We propose the use of machine learning models to enable non-invasive measurement of biomarkers such as Total Cholesterol, Uric acid and Blood Sugar. Several Machine learning algorithms, including Linear Regression, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Support Vector Regression (SVR), were applied to the datasets constructed using the MAX30102 sensor. The metrics used to evaluate regression models were Mean Square Error (MSE) and Coefficient of determination ( \(R^2\) ). The final prediction model was built using the algorithm that yielded the highest Coefficient of determination ( \(R^2\) ). A user-friendly interface was developed using Tkinter, allowing the input of sensor values from the MAX30102 sensor. The prediction of biomarker values promotes health awareness and timely alerts against potential complications. The results obtained using this approach were validated against laboratory blood reports, revealing an average offset of less than 10% in the predictions.