This dissertation presents an in-depth exploration of Radio Frequency Machine Learning (RFML) through the development of hardware-accelerated solutions to enhance RF communication systems. The primary focus is on employing Short-Time Fourier Transform (STFT) assisted Convolutional Neural Networks (CNNs) to innovate hardware accelerators for efficient signal processing. The research introduces a series of advancements including the application of STFT-CNN for Automatic Modulation Classification (AMC), which significantly improves the recognition accuracy and processing speed in RF communication systems. Furthermore, the work extends to developing autoencoder-based accelerators tailored for Orthogonal Frequency Division Multiplexing (OFDM) channel estimation and anomaly detection, thus bolstering IoT security. The dissertation highlights the critical integration of machine learning models with hardware design to combat adversarial evasion attacks effectively, showcasing a robust method to safeguard communication networks. Extensive simulations and hardware implementations confirm the efficacy of the proposed models, which outperform traditional methods in terms of speed, accuracy, and computational efficiency. This work not only pushes the boundaries of RFML applications but also sets a foundational approach for future advancements in the field of secure and efficient communication technologies.