<p>The increasing demand for high-performance 5G networks has driven the adoption of Filter Bank Multicarrier (FBMC) as a superior alternative to traditional OFDM due to its enhanced spectral efficiency and reduced out-of-band emissions. However, FBMC systems face challenges in channel estimation and interference cancellation caused by non-orthogonal subcarriers. This paper proposes a novel Recurrent Neural Network (RNN)-based Joint Channel Estimation and Interference Cancellation (JCEIC) method that leverages Long Short-Term Memory (LSTM) networks to exploit temporal correlations in doubly-selective channels, enabling accurate channel estimation and effective interference mitigation with low computational complexity. Our simulations demonstrate that the proposed approach significantly reduces the Bit Error Rate (BER), outperforming conventional methods—particularly at low SNRs, where FBMC achieves a BER below 0.1 at just 5 dB—while approaching ideal channel performance. By combining optimized pilot placement with deep learning-driven interference cancellation, this work provides a robust and scalable solution for 5G and beyond, bridging the gap between theoretical advancements and practical deployment in next-generation wireless systems.</p>