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In this article, we employ a machine learning (ML) approach for theestimations of four fundamental parameters, namely, the Hubble constant($H_0$), matter ($\Omega_{0m}$), curvature ($\Omega_{0k}$) and vacuum($\Omega_{0\Lambda}$) densities of non-flat $\Lambda$CDM model. We use $53$Hubble parameter values measured by differential ages (DA) and baryon acousticoscillations (BAO) techniques in the redshift interval $0.07 \leq z \leq 2.36$.We create an artificial neural network (called ParamANN) and train it withsimulated values of $H(z)$ using various sets of $H_0$, $\Omega_{0m}$,$\Omega_{0k}$, $\Omega_{0\Lambda}$ parameters chosen from different andsufficiently wide prior intervals. We use a correlated noise model in theanalysis. We demonstrate accurate validation and prediction by ParamANN.ParamANN provides an excellent cross-check for the validity of the $\Lambda$CDMmodel and alleviates the Hubble tension problem which has been reported earlierin the literature. We obtain $H_0 = 66.11 \pm 2.59$ $\rm{kmMpc^{-1}sec^{-1}}$,$\Omega_{0m} = 0.3359 \pm 0.0814$, $\Omega_{0k} = 0.0237 \pm 0.1248$ and$\Omega_{0\Lambda} = 0.6405 \pm 0.0861$ by using the trained network. Theseparameter values agree very well with the results of global CMB observations ofPlanck collaboration.

ParamANN: A Neural Network to Estimate Cosmological Parameters for $\Lambda$CDM Universe using Hubble Measurements