The accurate estimation of apple weight is crucial for the apple industry as it is essential for quality control, grading, and pricing. Using image processing andsupervised learning approaches, this research attempts to develop a system for calculating the weight of apples. Theproposed method involves capturing images of 164 apples ,extracting features such as color and texture from the images, and using these features as input variables in a multilinear and K-NN regression model to predict the weight of the apple. The features computed from theimages were analyzed using regression models, and the model's implementation was imposed using multiple metrics, such as R 2 score of .92 and .90 respectively achieved. The study's findings show that computer vision and machine learning have the ability to precisely estimatethe weight of apples, which could have implications for improving the efficiency and accuracy of apple sorting and packaging processes in the agriculture industry.