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An advanced image processing approach integrated with communication technologies and a camera for bin level detection has been presented. The proposed system is developed to overcome the environmental situation of bin and variety of waste being thrown inside it. Gray Level Aura Matrix (GLAM) approach is proposed to extract the bin image texture. The GLAM parameter such as neighboring system is investigated to determine the best parameters values. To evaluate the performance of the system, the extracted image is trained and tested using MLP and KNN classifiers. The results have shown that the bin level classification accuracies reach acceptable performance levels for class and grade classification with rate of 98.98% and 90.19% using MLP classifier and 96.91% and 89.14% using KNN classifier, respectively. The results demonstrated that the proposed system is a robust and can work with variety of waste and various bin situations.