Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63622
Volume:
10 |
5 |
May 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
MASTER OF COMPUTER APPLICATION
BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY
MASTER OF COMPUTER APPLICATION
BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY
This has been exacerbated by the urbanization rates as well as the growing populations, creating strain on the conventional waste management systems that have been shown to have a lot of inefficiencies related to labor and manual processes .To address the highlighted gaps, the project develops a Generative AI-based Waste Management Solution aimed at increasing the efficiency of all the stages that are involved from waste generation to final waste disposal. The solution combines the use of generative artificial intelligence technologies, as well as the latest innovations in the fields. Of the Internet of Things, computer vision, and predictive analytics, in order to create an intelligent, dynamic, and sustainable waste management system. Smart and IoT -enabled bins communicate data in real-time, and this data is leveraged by AI systems in order to fore cast trends in waste buildup in many different locations. This. data enables the system to generate dynamic waste .collection routes. This demand-driven. system cuts down on unnecessary movements of vehicles, reduces. fuel consumption, and reduces operational costs while performing waste collection efficiently and on time. The system also. provides analytical reports and interactive dashboards that provide key in sight son waste generation. trends,
KEYWORDS
The Generative AI for Waste Management and Recycling Solution presents a progressive and practical response to the increasing challenges associated with modern waste management. The development of the Generative AI–Driven Waste Management and Recycling Solution (GAI-WMRS) represents a fundamental transition from legacy, models to a Proactive Resource Recovery Framework. By integrating Generative AI with a Distributed IoT Fabric, this project confirms that municipal waste management can be transformed from a linear logistical burden into a dynamic, data-centric ecosystem. The successful synchronization of real- time telemetry and neural path finding logic proves that "Information Purity" is the key to urban sustainability. The empirical outcomes of this study under score the multi faceted benefits of Automated Waste Lifecycle Management. The mitigation of "Dead-Head" mileage. through elastic routing directly addresses the Carbon Intensity of municipal fleets, while the introduction of. Computer Vision-Based Segregation resolves the long-standing crisis of material cross-contamination. These advancements do more than. improve cleanliness; they foster a Bio-Sanitary Buffer that protects public health by eliminating overflow-driven pest vectors and reducing human exposure to hazardous contaminants.
1. S. V. T. Dao, “Artificial Intelligence Applications in Waste Management Systems,” Science Direct, 2025. 2. D. B. Olawade, “Smart Waste Management: A Paradigm Shift Enabled by Artificial Intelligence,” Science Direct, 2024. 3. A. S noun, “AI-Driven Innovations in Waste Management,” MDPI, 2025. 4. Fang, “Artificial Intelligence for Municipal Waste Management in Smart Cities,” Pub Med Central, 2023. 5. Samadhiya, “Exploring the Potentialof Artificial Intelligence in Municipal Waste Management,” Science Direct, 2025. 6. Sonawane, “Innovations in E-Waste ManagementandRecycling,”SSRN,2025. 7. G. Ahmad, “Intelligent Waste Sorting for Urban Sustainability Using Deep Learning,” Nature, 2025.
Publication Date
5/20/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
3
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...