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Modern wireless communication systems rely on independently designed transmitter and receiver components a modular approach that, while well- stablished, introduces significant complexity and limits adaptability to dynamic channel conditions. This project proposes and implements an intelligent end-to-end communication system using a deep learning-based Autoencoder architecture as an alternative to conventional modulation and coding designs. The proposed system models the transmitter as an encoder neural network and the receiver as a decoder neural network, with an Additive White Gaussian Noise (AWGN) layer representing the communication channel. By training the entire communication chain jointly through back propagation, the system automatically learns optimal signal encoding and decoding strategies without manual feature engineering or predefined modulation schemes. The implementation is built using Python, TensorFlow, NumPy, Matplotlib, and a Streamlit-based interactive web interface. Three optimization algorithms — Adam, RMSProp, and Gradient Descent — are evaluated across 10,000 training iterations with varying batch sizes (256, 512, 1024) and a training SNR of 7 dB. Adam achieved the fastest convergence with a final cross-entropy loss of 0.054, outperforming RMSProp (0.113) and Gradient Descent (0.421). Block Error Rate (BLER) analysis across SNR values from 0 to 14 dB confirms substantial improvement in communication reliability at higher SNR levels., versus 0.016 and 0.009 respectively.