Aims. Quasar catalogues from narrow-band photometric data are used in a variety of applications, including targeting for spectroscopic follow-up, measurements of supermassive black hole masses, or baryon acoustic oscillations. Here, we present the final quasar catalogue, including redshift estimates, from the miniJPAS Data Release constructed using several flavours of machine-learning algorithms. Methods. In this work, we use a machine learning algorithm to classify quasars, optimally combining the output of eight individual algorithms. We assess the relative importance of the different classifiers. We include results from three different redshift estimators to also provide improved photometric redshifts. We compare our final catalogue against both simulated data and real spectroscopic data. Our main comparison metric is the f1 score, which balances the catalogue purity and completeness. Results. We evaluate the performance of the combined algorithm using synthetic data. In this scenario, the combined algorithm out-performs the rest of the codes, reaching f1 = 0.88 and f1 = 0.79 for high- and low-z quasars (with z ≥ 2.1 and z < 2.1, respectively) down to magnitude r = 23.6. We further evaluate its performance against real spectroscopic data, finding different performances (some of the codes show a better performance, some a worse one, and the combined algorithm does not outperform the rest). We conclude that our simulated data are not realistic enough and that a new version of the mocks would improve the performance. Our redshift estimates on mocks suggest a typical uncertainty of σNMAD = 0.11, which, according to our results with real data, could be significantly smaller (as low as σNMAD = 0.02). We note that the data sample is still not large enough for a full statistical consideration.