To properly answer this question, we have to back to the basics of the meta-analysis methods employed. The analyses assume that the units (e.g. randomized patients) are independent from each other. In this case, they are not as the same patients were evaluated at different time points (e.g. 1, 3, 6 months, etc.). Therefore each patient/ study should be included in the analysis only once. That can be the longest follow-up reported for each study, it can be the longest follow-up across studies, etc. Clinical expectations also plays an important role here as the interventional effect may not be expected to last once the intervention is no longer given.
The above is one option but there are others. For example, you could decide to present each subgroup (e.g. time period) separately without pooling across the subgroups. You can also present bother (e.g. pooled effect at longest time period reported per trial + subgroups based on time periods without pooling across time periods) in separate forest plots.
Another option is to use a hierarchical model, or simulate it by first pooling studies with multiple time periods into a single study. For this not to have the same issues as before (e.g. unit of analysis error), you will have to divide the number of participants across the number of times the study is being included in the analysis (e.g. 30 patients over three time periods = 10 patients per time period). That will prevent over-estimating the precision of the trial after pooling. Of course, this method doesn't come without assumptions. For example you assume that length of following is independent from the effect estimates and the differences based on time periods is due to random chance.
In practice, we usually use the longest follow-up per trial and perform a subgroup analysis separately for hypothesis generation.