If you know the price levels of the two neigbouring data points, as well as their returns, then you can directly imply the missing return. However, I suspect this is not what you have, and it is quite an obscure corner case anyway. In addition, in real life you could easily have several missing values in sequence.
The naive, but not necessarily bad methods would be
Set the missing return to 0.0 or the risk-free interest rate (or strategy neutral value).
Skip missing months for all assets. However, might lead lack of remaining data.
Other more sophisticated ways to impute the values can be using e.g. regression, or methods that finds the most likely values based on the observed returns. One intuitive way to think about this is that you can estimate how all the assets move together (covariance/correlation), and then on a month where you have missing data, you observe most assets, and calculate the likely missing asset values based on the covariance/correlation matrix. (See Maximum Likelihood / Expected Maximization Algorithm in the stackexchange link)
For more detailed info and more advanced methods:
Handling Missing values in stocks returns when estimating the co variance matrix