in 6G era there is a high demand for different communication links. This results in multi-user interference scenario, in which will lead to downgrade in system performance and outage. In this thesis we try by estimating the main and interference channel at the first step, then prediction of the main and interference channel for the next time instances, gain the information about channel condition which helps us to take proper actions against interference. We are interested to investigate two ML architecture of CNN + BiLSTM and Transformer for channel estimation and prediction. We use Interpolation, LMMSE and AMLE as baselines.