AI-Based Dynamic Spectrum Prediction and Allocation for IoT Wireless Networks Using Python

The advent of the Internet of Things (IoT) has put pressure on scarce spectrum resources, especially in heterogeneous, interference-rich, and latency-critical environments. Static access regimes and rule-based ones cannot handle non-stationary interference and ultra-dense deployments. Leveraging advances in artificial intelligence (AI), this paper investigates and achieves spectrum intelligence: short-horizon spectrum prediction and dynamic, risk-aware allocation at radio timescales. We combine classical machine learning techniques, deep sequence and vision encoders (LSTM/GRU/TCN, spectrogram/REM models), transformers, reinforcement learning, and graph-based surrogates for channel–power assignment. Aside from modeling, reproducibility and deployability are also our focus with Pythonic pipelines: leakage-safe preprocessing, calibration (Brier/ECE), safety shields, and closed-loop evaluation with ns3/ns3-gym. Public datasets and simulators are inventoried with guidelines for splits and metrics (utilization, latency, violation rate, fairness, and energy). We cover engineering trade-offs for deployment on the edge (quantization, ONNX/TorchScript, federation with Flower) and outline open challenges in data sparsity, domain adaptation, explainability, security, and real-time feasibility. The result is an actionable roadmap to bring AI-enabled spectrum management from promising prototypes to robust, scalable IoT systems.