Accurate prediction of different components of a hydrological cycle is of central interest to hydrologists for guiding water-resources-related policies. Over the years, various modelling frameworks have been developed and tested worldwide. These modelling frameworks, generally, are classified into three types: physics-based or process-based, black-box or data-driven and conceptual models. The physics-based models try to represent the catchment scale physical processes using the equations governed by mass, momentum and energy balance principles. The data-driven models, on the contrary, focus on mapping the input to the output by employing linear or non-linear transfer functions that seldom consider the underlying physical process. The conceptual modelling frameworks that lie in between the above two frameworks focus on representing the dominant hydrological processes in simplified mathematical forms. According to a few recent studies, deep-machine learning-based models that come under the category of data-driven models outperform the well-established conceptual hydrological models. These studies reported that the deep-learning models can better capture the information available in the model input over the traditional conceptual models. However, since the deep-learning-based models are physically inconsistent, their applicability under unforeseen scenarios may lead to significant ambiguity in the forecast. Taking into consideration the pros and cons of the data-driven models, a framework called physics-guided machine learning (PhyML) has been gaining popularity in recent years. It is built on the notion of incorporating the physical process understanding in the data-driven models. There are several ways of building the PhyML, such as providing an output of a physics-based model as input to an ML-based model, applying mass balance constrain in ML models, etc. The contrasting approach is also possible where a component of the conceptual hydrological model is simulated using ML. The current chapter discusses the recent applications of PhyML in the field of hydrology and compares the performance of a PhyML model with its simple ML variant for a case of dry-weather flow prediction.