My model has 1 continuous outcome. Above is the residuals vs. fitted values plot. I know that if the equal variance assumption holds, then the residuals should be scattered evenly around 0 line with no discernible pattern. Here, it seems that there are no fitted values from 135 to 145. What can I conclude from this residual plot? Does homoscedasticity hold?
I don't see any reason to be concerned about heteroscedasticity. The absence of any predicted values in the interval $[135, 145]$ is a little weird, but not necessarily problematic, and isn't related to the issue of heteroscedasticity. Homoscedasticity just means that the vertical scatter of the points around the line is constant—it has nothing really to do with their horizontal spread (see here). Most likely there is a gap in $X$ that corresponds to the gap in $\hat y$ here.
Also, be aware that the nature of variance is that it will appear to spread out more where there is more data / a higher density of data, so I doubt the slight difference in spread between the left cluster and middle cluster of residuals means anything.
On the other hand, you have a single datum with a high fitted value that could be driving your results. I might be worried about that. You could check the leverage and Cook's distance values associated with that point (cf., here), or try fitting the model without it as a sensitivity analysis and see if the results are similar enough with respect to what you care about.