Digital communication gives larger data capacity and best communication speed. The signal transmitted through the wireless channels in digital communication affected by nonlinearities like noise. Inter Symbol Interference (ISI) dominantly causes huge data loss. It is caused due to multipath propagation, band-limited channels noise. Equalization, a process of inducing inverse channel response to compensate the effects of ISI is used to combat these effects. Orthogonal Frequency Division Multiplexing (OFDM) system supports high data rates and offers resistance towards the channel effect. Adding equalizer to OFDM systems make it more robust.In real time situations the response of channel is not known in prior. So, adaptive equalizers are implemented to compensate channel effects with the help of pilot symbols which are used to estimate the channel response and equalize the signal accordingly. Minimum Mean Square Error (MMSE), Least Square (LS) are some of the existing algorithms used to implement the equalizers. These conventional algorithms require some statistical properties of channel which is not possible in real time scenarios. Also, Linear Transverse equalizer failed to provide satisfactory results in the case of Non Minimum Phase channel. Neural Networks is capable to perform this task and can be used as an alternative to these algorithms.Back Propagation Neural Networks (BPNN) and Deep Neural Networks (DNN) can achieve better results when compared to conventional algorithms. Here, Deep Neural Networks with Adam optimization is proposed to enhance the performance of equalization. DNN can be used in both linear and non-linear channels. Also, Adam optimization used with Back Propagation uses variable and adaptive learning rate which reduces slow convergence and increases the efficiency of neural network unlike stochastic gradient descent which uses fixed learning rate.In this work the performance of proposed equalizer in OFDM systems is analyzed over flat and frequency selective fading of Rayleigh channels. The performance is measured in terms of Bit Error rate over SNR range -30dB to 25 dB. The results are compared with that of MMSE and it is observed that over the mentioned range of SNR the proposed equalizer achieved acceptable results and has low Bit error rate than that of MMSE in both flat and frequency selective fading channels.