In recent years, high-entropy nitride coatings have attracted significant attention due to their unique design concept and excellent comprehensive mechanical properties. However, the preparation and optimization of high-entropy nitride coatings is a time consuming process due to the large number of elements and simulations required to obtain accurate estimates of parameters. To solve that, machine learning and high-throughput preparation were conducted in this study to prepare and optimize a (TiZrNbCrSi)N high-entropy nitride coating by constructing a hardness algorithm model and a toughness algorithm model. High-throughput preparation was conducted using a magnetron sputtering multi-targets co-deposition system. Silicon wafer with a radius of 50 mm were used as the substrate. After ultrasonic cleaning, it was sent into a deposition chamber. During the deposition process, substrate bias voltage of ?270 V, Si target power of 50 W, Cr target power of 80 W, TiZrNb alloy target power of 200 W and N2 flow rate of 12 sccm were set to prepare a (TiZrNbCrSi)N high-entropy nitride coating in three stages for a total of 8 000 seconds. The prepared coating was divided into grids with a spacing distance of 3 mm, and the intersection area of the grids was treated as one component point, with a total of 78 component points. Then, the surface and cross-sectional morphology of the sample were observed using JSM-7800F field emission scanning electron microscope (FESEM) and Oxford XMax-80 energy spectrometer, and the elemental composition and content were analyzed. XRD detection was performed using a D8 ADVANCE X-ray diffractometer with a step size of 0.08° and a scanning range of 2θ from 20° to 80°. The mechanical properties of the coating were measured using the Hysitron nanoindentation instrument according to the continuous stiffness method. In order to reduce experimental errors and improve data reliability, five experimental measurements were conducted at each component point to obtain the average value, and the spacing between each indentation was not less than 50 μm. A Shimadzu HMV-G-FA micro Vickers hardness tester with a load of 1-2 N was applied to the coating using the indentation method to characterize the coating toughness. The toughness KIC was calculated by measuring the crack length and the half diagonal length of the indentation. Based on the data obtained from the above experiments, the training and testing sets were divided at a 4∶1 ratio. A total of 9 sets of hyperparameters were obtained by combining the different numbers of estimator (50, 100 and 150) and the different maximum depth (5, 10 and 15). Grid search was performed using 10 fold cross validation. Python was applied to establish a random forest hardness algorithm model and a toughness algorithm model based on the grid search method for component design and efficient screening. The (TiZrNbCrSi)N high-entropy nitride coating was successfully prepared on the surface of silicon wafer. The coating had a thickness of approximately 720 nm and exhibited a columnar crystal with FCC structure. The hardness distribution of the coating ranged within 12-28 GPa, and the toughness was 1-10 MPa.m1/2. The root mean square error for the hardness prediction results was 1.118 GPa, and that for toughness 1.292 MPa.m1/2. The hardness algorithm model and toughness algorithm model built by machine learning in this paper show a high accuracy in predicting the mechanical properties of the (TiZrNbCrSi)N high-entropy nitride coating system. The filtered result (Ti0.079Zr0.081Nb0.089Cr0.119Si0.068)N0.564 coating shows both high hardness and toughness, with a hardness of 25.6 GPa and a toughness of 8 MPa.m1/2.