This work presents a novel method known as the Red Deer Algorithm-Based Artificial Neural Network (RDA-ANN) for managing real-time voltage and harmonic control in a cascade H-bridge multilevel inverter (CHB-MLI) that is fed by photovoltaic (PV) panels. The RDA-ANN technique proposed here computes the switching angles in real-time for selective harmonic elimination (SHE) on the output voltage of a multilevel inverter (MLI). In the proposed approach, a comprehensive lookup table containing 7776 permutations of switching angles was first generated offline using RDA optimization for a three-phase, 11-level CHB-MLI with five PV panels operating across a voltage range of 30 V to 35 V. This dataset was subsequently used to train a feed-forward ANN model capable of predicting optimal switching angles corresponding to any real-time voltage measurements from the panels. The SHE-PWM approach based on RDA-ANN targets the elimination of the 5th, 7th, 11th, and 13th order harmonics. This algorithm guarantees that the intended fundamental voltage is achieved, even during fluctuations in the voltages of the panels caused by varying irradiation and temperature conditions, while effectively removing the unwanted harmonics. The findings, validated under multiple environmental scenarios, illustrate that the RDA-ANN-based SHE-PWM technique successfully eliminates the selected harmonics from the load voltage with a fundamental voltage error not exceeding 0.18%, and results in a low total harmonic distortion (THD) value that complies with the IEEE 519-2022 standard across all tested conditions.