Blood pressure (BP) is a crucial health element, the fluctuations of which may have profound implications for an individual's well-being. Traditional methods of measuring BP, such as cuff-based and invasive devices, are not only uncomfortable but also unable to provide continuous monitoring. In response to this need, we propose a cuffless, continuous, and noninvasive BP measurement system utilizing photoplethysmograph (PPG) signals and machine learning (ML) models. PPG, an optical volumetric measurement technique, can identify alterations in blood volume within the tissue's vascular network. In this study, PPG signals obtained from diverse individuals underwent preprocessing and feature extraction. Subsequently, a feature selection technique was employed to identify suitable features. These selected features were then employed to train and evaluate ML models. Ultimately, we identified optimal regression models for independent estimation of diastolic blood pressure (DBP) and systolic blood pressure (SBP). Our findings indicate that the random forest (RF) model, in combination with the SelectFromModel feature selection method, outperformed other models. This model yielded significant outcomes, achieving a root mean square error (RMSE) of 6.61 for DBP and 10.06 for SBP, highlighting its superior performance in BP estimation.