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We introduce a transformative sensing platform that integrates large\u2010scale photonic multiplexing with data\u2010driven analytics to overcome these constraints. Our modular, affordable glass\u2010chip whispering\u2013gallery\u2013mode (WGM) photonic architecture is designed to support simultaneous readout of up to 100 sensing channels comprising over 10 000 high\u2010Q microresonators, exceeding existing multiplexing capabilities by more than two orders of magnitude. The photonic system is reinforced by the novel, cross\u2010platform, deep learning framework BioCCF. It applies cross\u2010channel fusion in biosensing for the first time and enables harmonized operation across heterogeneous chips by leveraging domain adaptation. Using over 200 h of measurements from nine independent chips, we demonstrate chip\u2010independent multiplexed biosensing with 99.3% identification accuracy and relative quantification errors of 10 for immunoglobulin G mixtures. Beyond this validation, the platform supports multi\u2010analyte immunoassays, environmental monitoring, and distributed diagnostics, enabling aligned sensor networks with shared data infrastructures and continuously improving generalization of the common analyzing\u00a0framework.<\/jats:p>","DOI":"10.1002\/advs.78168","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T03:40:04Z","timestamp":1791517204000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Massively Multiplexed Photonic Chip With Cross\u2010Channel Fusion For Harmonized Biosensing"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0329-0763","authenticated-orcid":false,"given":"Ivan","family":"Saetchnikov","sequence":"first","affiliation":[{"name":"Radio Physics Department Belarusian State University Minsk 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