Objective To screen for key genes and important pathways common for different etiologies of acute kidney injury (AKI) by pathway-associated deep neural network and multiple machine learning algorithms. Methods AKI microarray datasets GSE30718, GSE37838, GSE53769, GSE108113, GSE125779, GSE99325, and GSE174020 downloaded from the Gene Expression Omnibus (GEO) database were merged, including 60 kidney samples from AKI patients and 79 kidney samples from healthy controls. They were divided (8∶2) into training sets and test sets, and were used to train and evaluate pathway-associated deep neural network and 4 machine learning algorithms, including least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and extreme gradient boosting (XgBoost), to screen for common key genes and pathways of different etiologies of AKI. The downloaded datasets GSE99340 and GSE1563 were merged, including 43 kidney samples from AKI patients and 36 kidney samples from healthy controls, which were used as external validation sets for LASSO model and nomogram performance test based on the final screened genes. The pathway-associated deep neural network and machine learning algorithms were evaluated using receiver operating characteristic curves, precision, recall, accuracy, and F1-score. The immune cell infiltration characteristics were explored in AKI via cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), and Pearson correlation coefficients were used to evaluate the correlation between the final screened common key genes and immune cell infiltration levels. Results The pathway-associated deep neural network trained by 5-fold cross validation produced an area under curve (AUC) of 0.914 5±0.007 0, a precision of 0.750 0±0.044 0, a recall of 0.923 1±0.048 0, an accuracy of 0.838 7±0.016 0, and an F1-score of 0.827 6±0.020 0 in the test set, yielding a robust and highly accurate classification performance for AKI, and identified key pathways and a subset of candidate genes. The 4 machine learning algorithms all achieved high discriminative performance for AKI in the test set with AUC≥0.860, precision≥0.750, recall≥0.800, and F1-score≥0.774, and screened 7 common key genes for AKI with different etiologies, including CD86, C-X-C motif chemokine ligand 10 (CXCL10), dynamin 2 (DNM2), proto-oncogene FOS, transcription factor 12 (TCF12), VGF nerve growth factor inducible (VGF), and A kinase anchoring protein 5 (AKAP5). Based on the final screened common key genes, the LASSO model had an AUC of 0.940 4 for the test set and an AUC of 0.944 4 for the external validation, and the model showed a very high discriminatory ability for the AKI, which demonstrated the overall regulatory performance of the genes. The nomogram constructed based on the screened 7 genes demonstrated the highest classification performance with an AUC of 0.928 9, validating the outstanding contribution and overall action performance of the screened individual genes. Immune cell infiltration analysis showed that there were significant differences in B cells naïve, mast cells activated, monocytes, macrophages M1, B cells memory, and dendritic cells activated between AKI samples and healthy control samples (all P＜0.05). Macrophages M1 and monocytes were positively correlated with CD86 and CXCL10, mast cells activated were positively correlated with FOS, and B cells naïve were negatively correlated with CD86 and CXCL10 (all P＜0.01). Mast cells activated were positively correlated with VGF and negatively correlated with CD86 and TCF12, while memory B cells were positively correlated with CD86 (all P＜0.05). Conclusion Strategy combining pathway-associated deep neural network and multiple machine learning classifiers can mine high-value key genes from high-dimensional, complex and heterogeneous transcriptomic data as potential targets for therapeutic interventions in AKI.