This paper presents a method to estimate broiler weight automatically using a computer vision approach and machine learning model. The data used in this paper were obtained using a broiler cage video and manual weight calculation on daily basis for 33 days. The broiler's image is processed using several image processing methods such as noise removal, adaptive thresholding, morphological operations, and image segmentation to generate morphological data for each broiler. The model development is carried out by utilizing morphological data of each broiler which increased along with the increase in weight.  Five features of broilers were used, namely area, perimeter, mean radius, maximum radius, and the major axis. The proposed model then was validated using a 10-fold cross-validation method. The proposed model performance is calculated using the RMSE metrics, which shows the SVR model has the best performance with an RMSE value of 144,7 grams.