Deep learning is playing a significant role in computer vision, particularly in various application areas such as environmental sanitation, which relies on image-based information. The accumulation of solid waste materials in different regions can lead to health problems for both humans and animals. An alternate technique of waste segregation is required since manual waste segregation is a very risky, laborious, and time-consuming operation. The alternative strategy needs to be economical and timeefficient. Even a minor classification error can result in a number of significant environmental harms. To address such problems, the use of deep learning techniques in combination with digital images of waste materials is crucial. In the field of image-based applications, deep Convolutional Neural Network (CNN) architectures have gained recognition for their excellent performance in classifying solid waste images. In this study, the VGG16 and InceptionV3 neural architectures are proposed for the classification of solid waste materials using images obtained from Bahir Dar and its surrounding environment. The model uses RGB 224×224 pixel colored images as input and extracts features from them, resulting in a 1024-dimensional feature representation. The collected digital images of solid waste materials undergo preprocessing using different techniques, such as noise reduction with bilateral filter techniques and segmentation using region-based segmentation techniques. A comparison is made between two CNN architectures, namely inceptionV3 and VGG16, and the testing accuracy results in 82.01% accuracy for VGG16 and 76.47% accuracy for inceptionV3. Based on this evaluation, VGG16 is selected as the superior model. Keywords; Deep Learning, VGG16, Inception, Solid Waste