As emissions regulations for transportation become stricter, it is increasingly important to develop accurate nitrogen oxide (NO _x ) emissions models for heavy-duty vehicles. However, estimation of transient NO _x emissions using physics-based models is challenging due to its highly dynamic nature, which arises from the complex interactions between power demand, engine operation, and exhaust aftertreatment efficiency. As an alternative to physics-based models, a multi-dimensional data-driven approach is proposed as a framework to estimate NO _x emissions across an extensive set of representative engine and exhaust aftertreatment system operating conditions. This paper employs Deep Neural Networks (DNN) to develop two models, an engine-out NO _x and a tailpipe NO _x model, to predict heavy-duty vehicle NO _x emissions. The DNN models were developed using variables that are available from On-board Diagnostics from two datasets, an engine dynamometer and a chassis dynamometer dataset. Results from trained DNN models using the engine dynamometer dataset showed that the proposed approach can predict NO _x emissions with high accuracy, where R ² scores are higher than 0.99 for both engine-out and tailpipe NO _x models on cold/hot Federal Test Procedure (FTP) and Ramped Mode Cycle (RMC) data. Similarly, the engine-out and tailpipe NO _x models using the chassis dynamometer dataset achieved R ² scores of 0.97 and 0.93, respectively. All models developed in this study have a mean absolute error percentage of approximately 1% relative to maximum NO _x in the datasets, which is comparable to that of physical NO _x emissions measurement analyzers. The input feature importance studies conducted in this work indicate that high accuracy DNN models (R ² = 0.92–0.95) could be developed by utilizing minimal significant engine and aftertreatment inputs. This study also demonstrates that DNN NO _x emissions models can be very effective tools for fault detection in Selective Catalytic Reduction (SCR) systems.