Agriculture is the mainstay of the Ethiopian and a key sector of its economy. However, Lack of productive and environmentally friendly technology is not widely applicable elsewhere. Likewise, the vermicomposting technology is a recently emerging science which not effectively transferred to the users across the country. Thus, this review is aimed to shine the importance, unexploited opportunities and the challenges hindered for further expansions in Ethiopian context. Vermicompost is among the powerful organic fertilizers which enhance soil Fertility and productivity. As agrarian society; there are plenty of raw byproducts and weed species which are not center of focus (i.e cattle waste, crop residues and invasive weeds like P.Juliflora, P.hysterophorus and E.crassipes respectively). However, there are many constraints there like policy issue, handling problems and sensitivity of the worms for different environmental factors which hindered to invest on a wide range. Generally, the number and weight of vermiculture as well as amount of produced casts are promised and every possible ways were recommended to entertain on the large scale.

Vermicomposting is one of the most important waste management techniques in the process of vermiculture. In this study, a Neural Network-assisted novel paradigm is proposed to predict waste from vermicomposting. The proposed Neural Network Skeleton is based on a Gallium Arsenide Processing Schema, which is used to separate wastes. By comparing the proposed sys-tem with existing methods, it was found that the proposed approach had the highest average prediction ratio of 91.32%, outperforming other techniques like ERD, RNN, and Deep LSTM. The separation ratio analysis also demonstrated the effectiveness of the proposed method, with a range of 45-94%. Furthermore, the study emphasizes the importance of chemical equilibrium and the effectiveness of our proposed Gallium Arsenide Processing Schema in achieving high prediction and separation accuracies, showcasing its potential for practical application in waste management processes. Lastly, the prediction of the process evolution stages is detailed, indicating the efficiency of the proposed system in achieving various levels of waste separation. Overall, the study provides valuable insights into the potential of the proposed methods in op-timizing wastewater management processes, paving the way for more effective and sustainable vermicomposting practices.