Nitrogen oxides (NOX) emissions that are caused by road traffic diesel engines affects public health. The existing instantaneous emissions models are often imprecise due to the lack of knowledge of highly non-linear processes behind real-world emissions and they do not include meteorological and driving volatility variables. This paper applied data mining techniques based on the Cross Industry Standard Process for Data Mining (CRISP-DM) method to a dataset of four diesel Euro 6 passenger cars tested in real-world driving conditions to: a) model stabilised hot NOX emissions based on kinematic (speed), internal engine (engine coolant temperature, engine load, engine speed, intake air temperature, manifold absolute pressure and mass air flow), meteorological (humidity) and driving volatility (acceleration and vehicular jerk); b) compare the performance of different machine learning (ML) techniques in predicting NOX emission rates, namely: Artificial Neural Networks (ANN), Random Forest (RF), and Gradient-Boosted Trees (GBT). The model that utilizes a set of detailed variables, particularly engine coolant temperature, engine load, engine speed, intake air temperature, humidity, acceleration and vehicular jerk, and using ANN technique was better able to deal with variability in emission data than models based on a single set of these variables. It was also found that models produced high Root Mean Square Error due to their inability in predicting high peaks in measured emission data. The presented models rely on fast inference times and can therefore be deployed for engine control units to inform drivers about their NOX emissions during driving.