Precise temperature control in high-temperature furnaces is challenged by nonlinearities, parameter drift, and high thermal inertia. This study proposes an adaptive control strategy combining a classical PID loop with real-time gain updates from a feed-forward artificial neural network (ANN). Implemented on an 18 kW retrofitted Blue-M furnace, the system was characterized by second-order transfer functions for heating and forced convection cooling. A dataset of 9702 samples was built from eight fixed PID configurations tested under a multi-ramp thermal profile. The selected 3-64-64-32-2 ANN, executed in Python and interfaced with LabVIEW, computes optimal gains in 0.054 ms while preserving real-time monitoring capabilities. Experimental results show that the ANN-assisted PID reduces the mean absolute error to 5.08 °C, limits overshoot to 41% (from 53%), and shortens settling time by 20% compared to the best fixed-gain loop. It also outperforms a fuzzy controller and remains stable under ±5% signal noise. Notably, gain reversals during cooling prevent temperature spikes, improving transient response. Relying on commodity hardware and open-source tools, this approach offers a cost-effective solution for legacy furnace upgrades and provides a replicable model for adaptive control in high-temperature, safety-critical environments like metal processing, battery cycling, and nuclear systems.