We present a novel code, named SIESTA (Statistical matchIng between rEal and Synthetic sTellar popuLations), designed for performing statistical isochrone fitting to colour-magnitude diagrams (CMDs) of single stellar populations by leveraging comparisons between the observed stellar distribution and predictions from synthetic populations, simulated on top of a grid of isochrones. These synthetic populations encompass determinant factors such as the cluster's initial mass function (IMF), the presence of non-resolved binaries, as well as the expected photometric errors, and observational completeness (or the observed luminosity function). Employing Markov Chain Monte Carlo within a Bayesian framework, SIESTA allows for the determination of a cluster's age, metallicity, distance, colour excess, and binary fraction (with masses exceeding a certain ratio). In this study, we rigorously benchmark the SIESTA code utilizing synthetic populations and evaluate its performance against observations from the VISCACHA Survey in the Small Magellanic Cloud, focusing on five star clusters: Lindsay 114, NGC 152, Lindsay 91, Lindsay 113, and NGC 121. These clusters were chosen for their diverse age range, spanning from 0.04 to 10 Gyr. Our findings demonstrate the capability of the SIESTA code to accurately represent the observed CMDs of these clusters. Furthermore, we compare the results obtained with SIESTA to previous characterizations of these clusters, highlighting the consistency between the derived metallicity and spectroscopic determinations from various sources.