Non-orthogonal multiple access (NOMA) approach has gained popularity in recent years. Additionally, it has shown promise as a technology for wireless communication systems up to and including the fifth generation (5G). In this paper a novel Spiking Neural Network-Based NOMA-OFDM Processing Under Weibull Fading for Accurate Uplink Signal Prediction has been proposed. In the offline stage, labeled NOMA-OFDM signals are passed through a Weibull fading channel to generate training data and corresponding labels, which are then used to train the SNN for accurate prediction. In the online stage, real- time NOMA-OFDM signals undergo the same Weibull fading channel effect, and the processed signals are fed into the trained SNN to produce predictions without requiring label information. This approach enables robust channel estimation and signal detection by leveraging the adaptability and learning capability of the SNN.