The lack of robust water quality data in drinking water services in many low-income settings can be attributed to inadequate funding for regular monitoring using analytical equipment. Turbidity is an indicator that is relatively quick and easy to measure; however, it still requires a turbidimeter and a trained operator. This study developed an entire smartphone camera-based application to measure turbidity in drinking water, removing both the need for external equipment and skilled labour. The application was created using a convolutional neural network, able to classify water samples into eight turbidity bins ranging from 0 to 40 NTU. The turbidity of the samples was created using formazine and kaolin clay suspensions. The in-built camera of a smartphone was used to capture images of water samples with known turbidity values. This algorithm was then embedded in a smartphone application, thereby providing an easy-to-use tool for users to estimate turbidity. Specifically, the protocol for using this application was developed with the intention that it will be used in low-resource settings by laypersons. Formazine samples achieved a turbidity classification accuracy of 98.7%, while kaolin clay samples achieved 90.9% accuracy using this method, which provides an encouraging proof of concept, as justification for further testing and improvements.