The article provides a comprehensive review of AI models and tools used in Industrial Internet of Things (IIoT) applications, focusing on their integration into embedded platforms. It highlights the importance of AI in reducing latency, energy consumption, improving data security, and enhancing system resilience in IIoT systems. The review covers various AI models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Generative Adversarial Networks (GANs), and Autoencoder Neural Networks. Each model is discussed in detail, including their architecture, applications, and performance in IIoT contexts. The article also examines software and hardware tools like Vitis AI v3.5, TensorFlow Lite Micro v2.14, and STM32Cube.AI v9.0, which facilitate the deployment of AI models on low-power devices. The review aims to provide a holistic understanding of AI-enabled IIoT systems and their practical implications in industrial settings.The article provides a comprehensive review of AI models and tools used in Industrial Internet of Things (IIoT) applications, focusing on their integration into embedded platforms. It highlights the importance of AI in reducing latency, energy consumption, improving data security, and enhancing system resilience in IIoT systems. The review covers various AI models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Generative Adversarial Networks (GANs), and Autoencoder Neural Networks. Each model is discussed in detail, including their architecture, applications, and performance in IIoT contexts. The article also examines software and hardware tools like Vitis AI v3.5, TensorFlow Lite Micro v2.14, and STM32Cube.AI v9.0, which facilitate the deployment of AI models on low-power devices. The review aims to provide a holistic understanding of AI-enabled IIoT systems and their practical implications in industrial settings.