This article addresses the issue of modeling and step analysis of employing QPSK, 16-QAM, 64-QAM, 256-QAM, and OFDMA schemes with a directed emphasis on systems BER as a standard of SNR. The simulation produces QAM symbols, assumes different levels of noise, and calculates the BER of every modulation type for varying SNR levels. For simulation purposes, an artificial neural network (ANN) is also introduced to classify the noisy 16 QAM and OFDMA signals. The BER comparison shows that QPSK is much better at low SNR, while larger constellations of 16-QAM and 64-QAM have higher BER but higher data rate capabilities. OFDMA combined with 64-QAM subcarriers experiences a minor performance decline because of the additional overhead involved. The ANN achieves real-time classification of the modulated signals, and this proves that machine learning techniques can be applied in communication systems. Graphs of BER against SNR and confusion matrix are used to display the results of experiments and simulations to give the relationship between data rate and error performance in several wireless technologies.