The collection of tax is the mainsource of incomefor the government. Taxcollecting has been associated with a lot of fraud, which is a challenge to detect.Fraud involves one or more persons who intentionally act secretly to deprive thegovernment of income and use it for their benefit. This study was initiated to explorethedeeplearningtechnologyfordevelopingmodelsthatcandetecttaxfraudusing dataobtained fromtheMinistryofRevenuesinEthiopia. To collect the data, the researcher used interviews and observation as primary dataand database analysis as secondary data. The dataset used in this study had beentakenfromEthiopia's Ministryof Revenues.Afterselectingthedataset,pre processing techniques such as filling missing records, removing outliers, reducingthe dimension, selecting the most relevant features, and finally normalizing thedataset input using features scaling are performed. The deep learning models for taxfrauddetectionareimplementedusingPythonprogramminglanguage.Theexperime nts had beenconducted by using the 23536-dataset records.We used 80%of the dataset for training the model and the remaining 20% of the dataset for testingthe performance of the model that is developedby the ConvolutionalNeuralnetwork. The model had shown the highest classification accuracy of 84.64%. Thenthis model was tested by 4708 testing datasets and scored a prediction accuracy of84.41%. The results of this study have shown that deep learning technology isvaluablefortaxfrauddetection.