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The Hubble constant ($H_0$) is one of the fundamental parameters incosmology, but there is a heated debate around the $>$4$\sigma$ tension betweenthe local Cepheid distance ladder and the early Universe measurements. Stronglylensed Type Ia supernovae (LSNe Ia) are an independent and direct way tomeasure $H_0$, where a time-delay measurement between the multiple supernova(SN) images is required. In this work, we present two machine learningapproaches for measuring time delays in LSNe Ia, namely, a fully connectedneural network (FCNN) and a random forest (RF). For the training of the FCNNand the RF, we simulate mock LSNe Ia from theoretical SN Ia models that includeobservational noise and microlensing. We test the generalizability of themachine learning models by using a final test set based on empirical LSN Ialight curves not used in the training process, and we find that only the RFprovides a low enough bias to achieve precision cosmology; as such, RF istherefore preferred over our FCNN approach for applications to real systems.For the RF with single-band photometry in the $i$ band, we obtain an accuracybetter than 1\% in all investigated cases for time delays longer than 15 days,assuming follow-up observations with a 5$\sigma$ point-source depth of 24.7, atwo day cadence with a few random gaps, and a detection of the LSNe Ia 8 to 10days before peak in the observer frame. In terms of precision, we can achievean approximately 1.5-day uncertainty for a typical source redshift of $\sim$0.8on the $i$ band under the same assumptions. To improve the measurement, we findthat using three bands, where we train a RF for each band separately andcombine them afterward, helps to reduce the uncertainty to $\sim$1.0 day. Wehave publicly released the microlensed spectra and light curves used in thiswork.

HOLISMOKES -- VII. Time-delay measurement of strongly lensed Type Ia supernovae using machine learning