Does autocorrelation imply temporal dependence? It is always said that a time series should be stationary for forecasting using classical methods.
While converting a time series to stationary, we detrend and deseasonalize the time series.
Does this imply that time series does not contain any autocorrelation after being stationary?

No, a stationary TS can still have a ACF showing a temporal dependency. Autocorrelation is the dependency of on point on the previous ones. This temporal dependency can be a drift or an oscillation and those parts will indeed be removed by making it stationary. But you can still have a dependency on the previous point, if your points are not independent one from another.
E.g. think of rain or temperature measured each hour. Lets make it stationary: we take out seasonal temperature variation and say, climate change leading to a slow temperature increase. Still, if last hour you had a certain temperature, it will be unlikely that you will have a completely different temperature now. So you still have temporal dependency, but it is dependent on only the last points.
You might want to look at ARMA models to understand that.
