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With the unprecedented increase of known star clusters, quick and moderntools are needed for their analysis. In this work, we develop an artificialneural network trained on synthetic clusters to estimate the age, metallicity,extinction, and distance of $Gaia$ open clusters. We implement a noveltechnique to extract features from the colour-magnitude diagram of clusters bymeans of the QuadTree tool and we adopt a multi-band approach. We obtainreliable parameters for $\sim 5400$ clusters. We demonstrate the effectivenessof our methodology in accurately determining crucial parameters of $Gaia$ openclusters by performing a comprehensive scientific validation. In particular,with our analysis we have been able to reproduce the Galactic metallicitygradient as it is observed by high-resolution spectroscopic surveys. Thisdemonstrates that our method reliably extracts information on metallicity fromcolour-magnitude diagrams (CMDs) of stellar clusters. For the sample ofclusters studied, we find an intriguing systematic older age compared toprevious analyses present in the literature. This work introduces a novelapproach to feature extraction using a QuadTree algorithm, effectively tracingsequences in CMDs despite photometric errors and outliers. The adoption ofANNs, rather than Convolutional Neural Networks, maintains the full positionalinformation and improves performance, while also demonstrating the potentialfor deriving clusters' parameters from simultaneous analysis of multiplephotometric bands, beneficial for upcoming telescopes like the Vera RubinObservatory. The implementation of ANN tools with robust isochrone fittechniques could provide further improvements in the quest for open clusters'parameters.

Parameter Estimation for Open Clusters using an Artificial Neural Network with a QuadTree-based Feature Extractor