Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3434 2019 2019 Research Scholar, Department of ECE, Chaitanya deemed to be University, Hyderabad, India Abhiram Abhiram Professor & Head, Department of ECE, Chaitanya deemed to be University, Hyderabad, India Seetharam Khetavath With the growing demand for efficient image processing in embedded systems, the exploration of deep learning-based image compression methods has emerged as a promising avenue. Traditional image compression techniques, such as JPEG and PNG, face challenges in achieving optimal performance for constrained environments due to their reliance on handcrafted algorithms and limited adaptability. This study investigates the use of deep learning models for image compression tailored to embed systems, focusing on encoder and decoder architectures. By leveraging convolutional neural networks (CNNs) and variational auto encoders (VAEs), we design lightweight models capable of achieving high compression ratios while maintaining visual fidelity. The research emphasizes computational efficiency, ensuring compatibility with the resource constraints of embedded hardware. Key contributions include the development of streamlined architectures optimized for low memory and power usage, along with a comprehensive evaluation of compression quality, reconstruction accuracy, and real-time performance. The results demonstrate that deep learning-based approaches can outperform traditional methods in terms of adaptability and efficiency, paving the way for their integration into next-generation embedded systems. 2025 2025 37 52 10.54216/JISIoT.150104 https://www.americaspg.com/articleinfo/18/show/3434