Aiming at improving the accuracy and real-time performance of zoom imaging depth measurement, based on the given system design configuration, a new monocular visual depth measurement method with liquid optical control is proposed by utilizing liquid lens adjustment characteristics and neural network technology. Firstly, to eliminate the influence of optical axis drift induced by the liquid gravity factor on the measurement results, the ratio of target image area is adopted as the feature parameter. A target area calculation method based on chain code classification and strip segmentation is presented. Then, in order to describe the mapping relationship between liquid lens parameters, image feature quantity and target depth, a neural network model of liquid monocular depth measurement is constructed, and the model parameters are optimized by genetic algorithm. Furthermore, the focal power function is obtained by calibrating the parameters of the liquid lens. The neural network trained on the dataset for depth measurement has an average prediction relative error of 0.799%. Finally, an experiment is designed to test and verify the method. The average depth measurement error of targets with different distances is 2.86%, and the average measurement speed is 108.2 ms. The measurement error for targets of different shapes at a distance of 1 000 mm shall not exceed 3.60%. The results show that the monocular vision method combining liquid optical control and neural network prediction can achieve high-precision and fast depth measurement, and has good generalization performance for different shapes of objects. The research provide a new technical idea for overcoming the existing limitations of zoom imaging ranging method.