This article presents a groundbreaking approach to intrusion detection for IoT-based electric vehicle charging stations (EVCS), integrating convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent units (GRU) models. The proposed framework leverages a comprehensive real-world cybersecurity dataset, specifically tailored for IoT and IIoT applications, to address the intricate challenges faced by IoT-based EVCS. Extensive testing in both binary and multiclass scenarios demonstrates remarkable results, achieving 100% accuracy in binary classification, 97.44% accuracy in six-class classification, and 96.90% accuracy in fifteen-class classification. These achievements highlight the efficacy of the CNN-LSTM-GRU ensemble architecture in creating a resilient and adaptive intrusion detection system for IoT infrastructures. The ensemble algorithm, accessible via GitHub, represents a significant stride in fortifying IoT-based EVCS against a diverse array of cybersecurity threats. Intrusion Detection Systems (IDS), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Internet of Things (IoT), Electric Vehicle Charging Stations (EVCS), Cybersecurity, Deep Learning, Ensemble Learning, Real-Time Data Processing, Resource Efficiency, Cyber Threat Adaptability.This article presents a groundbreaking approach to intrusion detection for IoT-based electric vehicle charging stations (EVCS), integrating convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent units (GRU) models. The proposed framework leverages a comprehensive real-world cybersecurity dataset, specifically tailored for IoT and IIoT applications, to address the intricate challenges faced by IoT-based EVCS. Extensive testing in both binary and multiclass scenarios demonstrates remarkable results, achieving 100% accuracy in binary classification, 97.44% accuracy in six-class classification, and 96.90% accuracy in fifteen-class classification. These achievements highlight the efficacy of the CNN-LSTM-GRU ensemble architecture in creating a resilient and adaptive intrusion detection system for IoT infrastructures. The ensemble algorithm, accessible via GitHub, represents a significant stride in fortifying IoT-based EVCS against a diverse array of cybersecurity threats. Intrusion Detection Systems (IDS), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Internet of Things (IoT), Electric Vehicle Charging Stations (EVCS), Cybersecurity, Deep Learning, Ensemble Learning, Real-Time Data Processing, Resource Efficiency, Cyber Threat Adaptability.