Smart garbage classification for recycling utilizes machine learning algorithms to classify waste materials like plastic, paper, and metal into their respective categories. This involves training a model with labeled images and using it to predict new images. The use of oneAPI Deep Neural Network Library (oneDNN) is critical to improve recycling efficiency and accuracy. oneDNN optimizes deep learning operations, leading to faster execution times and better performance on modern CPUs. This optimization is crucial in recovering more recyclable materials, reducing human error, and improving accuracy. Overall, oneDNN has significant implications for promoting a sustainable future through improved recycling processes.