This research focuses on the application of various algorithm models in the information data management of waste materials, aiming to solve the problems of low efficiency and insufficient accuracy in traditional data processing. In this study, machine learning algorithm and big data technology are used to analyze and optimize the classification, storage, and recycling of waste materials. Firstly, through deep learning and convolutional neural network technology, the characteristics of waste materials are effectively extracted and identified to improve the accuracy of classification. Then, using big data analysis technology, the circulation and utilization of waste materials are statistically analyzed to optimize resource allocation and improve resource utilization efficiency. In addition, this research also combines the data mining and predictive analysis technology to establish a complete set of waste materials information management system, which can monitor the flow of waste materials in real time and predict the future demand trend of resources. The experimental results show that the system has obvious advantages in improving the efficiency of waste material classification and recycling and reducing the cost. The research results show that the information data management mode of waste materials combined with various algorithm models has higher efficiency and application potential. In the experiment of prediction function model, compared with the original waste materials input system, the efficiency has increased by 9.4%. It provides a new idea for efficient management and sustainable development of waste materials.