Firstly, yes, this can be done. And it is probably best to try to get everything to the hazard ratio scale, because one would guess there's a good reason why time-to-event analyses were done in the first place (e.g. not all patients complete trial, event driven trials where not all patients were followed for the same duration etc.).
If you have a log-hazard ratio with standard error (easy to backcalculate from confidence intervals or p-values, if not given, or approximately from the total number of patients with an event), then everything is great and a standard approach would be to do an inverse variance (or corresponding random effects) meta-analysis of the estimate (log-hazard ratio) and its standard error.
Problems arise, if you do not have that information for all trials. It is the easiest to deal with, if you have a relatively constant hazard rate for events over time, in which case you can approximately assume an exponential time-to-event model. You can get an idea off whether that is appropriate, if some of the trials give you e.g. a Kaplan-Meier plot. Alternatively, you can think about whether it is plausible: e.g. if all patients are relatively stable hypertensive patients with a blood pressure of 160 to 170 before we start following them, then there is probably no reason why their hazard rate for death should differ too much or change much over time. In contrast, if we say start following patients the day after which they just survived a myocardial infarction, then there is probably much more of a risk that they died shortly thereafter (say within 30 days) and then the risk goes down over time.
If you have the number of patients with an event $y_{ij}$ in each treatment group $j=0,1$ of trial $i$ and the follow-up to first event or censoring $t_{ij}$ (i.e. summed up over all the patients), then you have a likelihood for that trial of
$$ (\exp(\text{log-HR}) \lambda_i)^{y_{ij}} \exp(e^{\text{log-HR} \times j} \lambda_i t_{ij}), $$
where $\text{log-HR}$ is the log-hazard ratio and $\lambda_i$ is a nuisance parameter for the control group hazard rate. In contrast, for the trials, for which you have the log-hazard ratio and its standard error, the likelihood is a normal pdf $\phi(\text{log-HR}), \text{SE}^2)$.
If you do not have this information, then this paper on the meta-analysis of aggregate data on medical event occurrence may be relevant and discusses a number of relevant options.
Getting from a hazard ratio and no further information back to the number of patients with an event is more or less impossible.
Finally, you always have the option of contacting the authors for extra information, checking clinical trial registries (and results databases), health authority briefing books, approved drug labels and so on.
