Shale shakers are crucial in drilling operations, separating solid–liquid mixtures, protecting equipment, improving efficiency, and supporting environmental protection and resource recovery. Typically, the solid–liquid separation status on shaker screens is assessed through manual inspection and experiential judgment, with the screen tilt angle adjusted manually. This method has inherent delays, failing to promptly detect and address screen surface abnormalities. To overcome this, a computer vision-driven intelligent control system has been proposed, enhancing drilling sieving operations’ efficiency. The system uses computer vision and servo motors to monitor and adjust the sieving state. Twin industrial cameras capture images, which undergo preprocessing like perspective correction, grayscale conversion, noise reduction, and illumination equalization. Local threshold segmentation and morphological analysis distinguish between liquid and solid states. The segmented images’ contours and area analysis precisely determine the separation state. Results are transmitted to motors via an industrial computer to adjust the sieve net angle. Servo motors on both sides manipulate the sliding block lifting mechanism for precise tilt adjustment. Experiments demonstrate that the method achieves an average accuracy of 93.635% in identifying mud edges. The system’s high efficiency and reliability ensure accurate identification and adjustment of the solid–liquid separation state, thereby optimizing drilling sieving workflows.