Viticulture and oenology are vulnerable to climate change, which affects key quality parameters such as Brix degree and density. These parameters are critical for making industrial decisions about accepting raw materials and ensuring optimal final product quality. This study aims to predict density and Brix degree using a digital prototype with infrared technology (VL53L0X distance sensor) and strain gauges (weight sensor), implemented on an Arduino platform. Calibration of the VL53L0X sensor was performed using a tinted sugar solution (14°Brix) at various volumes, resulting in a linear equation: Volume = −0.5147(Height) + 149.39 (R2 = 0.9934). Six concentrations of grape must and sugar (17.3, 21.1, 24.8, 29, 33, and 37.1°Brix) were prepared and measured at 19 °C, showing a high correlation between the prototype and standard pycnometer measurements (r2 = 0.9872). The prototype provides highly reliable density values for red grape musts, with a strong positive correlation to traditional methods (r2 = 0.9874). It also predicts the Brix degree of grape must from its density using a predictive mathematical model, improving wine quality in the industry.