I haven't been following the field any more in the past years, but I think you would need to include more predictors than the sequence alone for this model to produce reliable output.

Firstly, the genomic context (cis-regulatory element, promoter, gene body, intergenic etc.) and cell type surely matters, so your model would need to be trained on relatively long continuous sequences and unaligned short reads would likely not suffice. Additionally, the model needs to consider the state of the cell (mitotic age and potentially senescence, cancer etc.) or will be limited to predict methylation for one kind of population. Lastly, there have been long debates about the fraction of "functional" methylation in the cell, with estimates as low as 20%. So expect a lot of randomness and noise.

Two papers you should have a look at are this review from on the dynamics of methylation during mammalian development and how a complex interplay of transcription factors shapes the methylome. If you're masochistically inclined and can stand long, dreary monologues, you can additionaly have a look at Chapter 12, sections 2 and 3 of my thesis (PDF download), where I discuss all kinds of methylation features and their causes known up till 2019. This was meant to cover up the sad truth of me failing to discover something noteworthy in a crappy 3 vs. 3 sample WGBS dataset despite wasting 8 years of my life with it.

Not my area, but there's a random forest classifier that mixes genomic and methylation features, including SNPs (Zhang et al., 2015. Genome Biology).

And DeepCpG was trained for imputation on scRNA, but they describe a module that predicts methylation from DNA sequence alone (AngerMeuller et al., 2017. Genome Biology). From the repo, the main model input seems to be sparse CpG methylation for imputation. Maybe you could adapt it for your purpose.

As an aside, I would be surprised if complete methylation imputation from DNA alone is reliable enough for most downstream questions.