Abstract: The proliferation of fentanyl analogs, marked by their structurally diverse and rapidly evolving molecular frameworks, presents formidable and urgent challenges to traditional detection methods. The existing techniques based on gas chromatography-mass spectrometry (GC-MS) are evidently limited by the subjective judgment of operators. The existing solutions all have obvious shortcomings. First, the manual judgment leads to the low efficiency of the entire data processing workflow. Second, the limited coverage and frequent delay of database make it unable to promptly incorporate newly emerging molecules. Third, the entirely single-point detection fails to form effective linkage, resulting in a severe issue of data silos. To overcome these deficiencies, this study specifically introduced an intelligent detection system based on a scalable cloud platform. The system combines three advanced techniques: large-scale artificial intelligence (AI) models, proprietary high-speed spectral matching algorithms, and an innovative federated retrieval framework. This integration enables a fully automated, one-click analytical workflow that fundamentally redefines the detection process. It supports multimodal inputs including raw analytical data, analytical reports, and spectral images captured by smartphones. Once data have been input, the system will automatically perform high-precision information extraction, feature analysis, and unambiguous substance identification without manual intervention. This study also conducted comprehensive and systematic testing in order to verify the overall performance of the developed system. Core parameters, including analytical accuracy, processing speed, sample throughput, and computational resource utilization, were quantitatively evaluated in the testing process. The results confirm that the system is fully capable of meeting the diverse requirements of rapid on-site sample testing. Additionally, a series of performance comparison tests were examined on the cloud platform hosts with varied performance levels. These hosts allow users to select servers tailored to specific needs, thus balancing performance and cost-effectiveness. This newly-designed system also incorporates a federated search algorithm, thereby establishing a cloud-client collaborative computing mode for detection tasks. When the algorithm is activated, the central processor searches the main path library, while distributing retrieval and matching tasks to associated clients, then the clients join in the matching and searching process. These processes achieve rapid coverage of newly emerged types of unknown controlled samples—especially under the circumstance that the main library lacks real-time updated sample data. This node-distributed network retrieval model can avoid sharing sensitive proprietary raw data, while breaking down the data silos under secure conditions. However, the operation of the algorithm will increase the overall system retrieval time, so it is necessary to carefully consider the actual needs and use the algorithm selectively. This intelligent system provides an effective solution for transforming the traditional mode of drug analysis into an intelligent multi-point collaborative detection approach, while presenting immediate and powerful tools for curbing the spread of fentanyl analogs. It can also offer some similar solutions for other scenarios related to drug control. This study effectively contributes to the integration of artificial intelligence with analytical chemistry, demonstrating the advent of an upcoming intelligent and data-driven era of analysis.