Occupant body size in vehicles varies significantly, encompassing differences in height, mass, and overall body composition. Adaptive restraint systems, featuring adjustable parameters such as belt load limiters, steering column load limiters and stroke, seat pan stiffness, and airbag pressure, can offer more equitable protection tailored to individual body sizes. In this study, a test rig modeled after the Volvo XC90 (2016) was used to collect data from 46 participants who were dressed in typical summer clothing and seated upright, without slouching or leaning sideways. Stepwise adjustments of the seat pan and seatback were performed. The collected measurements include seat pan movements (front-back and up-down), seatback recline, and key seatbelt-related parameters, such as belt payout length, D-ring angle, lap belt length, and buckle tension. The collected data was then used to train machine learning models to predict individual occupant characteristics: standing height, mass, and seated height. This study shows the challenges and opportunities for occupant body size estimation from seatbelt and seat location inputs. The prediction’s root mean square error across validation dataset was as follows: standing height 8.76cm, mass 11.33kg, and seated height 5.61cm. The prediction of mass fulfilled the defined criterion, while the prediction accuracy for standing height and seated height require further improvement. Our analysis reveals that a key improvement could be achieved by implementing an upgraded lap belt position sensor, given that lap belt length was identified as a dominant feature across models. Furthermore, the analysis suggests that D-ring angle, buckle tension, and seatback recline can be excluded from the input feature set.