The detection of low-surface-brightness galaxies beyond the Local Group poses significant observational challenges, yet these faint systems are fundamental to our understanding of dark matter, hierarchical galaxy formation, and cosmic structure. Their abundance and distribution provide crucial tests for cosmological models, particularly regarding the small-scale predictions of ΛCDM. We present a systematic detection and classification framework for unresolved dwarf galaxy candidates in the large-scale Ultraviolet Near Infrared Optical Northern Survey (UNIONS) imaging data. The main survey region covers 4861 deg2. Our pipeline preprocesses UNIONS data in three (gri) of the five bands (ugriz), including binning, artifact removal, and stellar masking before employing the software MTOBJECTS (MTO) to detect low-surface-brightness objects. Following a set of parameter cuts using known dwarf galaxies from the literature and cross-matching between the three bands, we were left with an average of ∼360 candidates per deg2. With ∼4000 deg2 in g, r and i, this amounts to ∼1.5 million candidates that form our GOBLIN (Galaxies OBserved as Low-luminosity Identified Nebulae) catalog. For the final classification of these candidates, we finetuned the deep learning model ZOOBOT, which was pretrained based on labels from the Galaxy Zoo project. We created our training dataset by visually inspecting dwarf galaxy candidates from existing literature catalogs within our survey area and assigning probability labels based on averaged expert assessments. This approach captures both consensus and uncertainty among experts. When applied to all detected MTO objects, our method identified 42 965 dwarf galaxy candidates with probability scores of >0.8, of which 23 072 have probabilities exceeding 0.9. The spatial distribution of high-probability candidates reveals a correlation with the locations of massive galaxies (log (M∗/M) ≥ 10) within 120 Mpc. While some of these objects may have been previously identified in other surveys, we present this extensive catalog of candidates, including their positions, structural parameter estimates, and classification probabilities, as a resource for the community to enable studies of galaxy formation, evolution, and the distribution of dwarf galaxies in different environments.We thank the referee for the constructive report, which helped to clarify and improve the manuscript. O.M. and N.H. are grateful to the Swiss National Science Foundation for financial support under the grant number PZ00P2_202104. N.H. thanks Stephen Gwyn for the help provided with questions regarding the UNIONS data. N.H. also thanks Jean-Charles Cuillandre for clarifying the nature of the surface brightness limit for the UNIONS r band data. E.S. is grateful to the Leverhulme Trust for funding under the grant number RPG-2021-205. D.C. is grateful for the financial support provided by the Harding Distinguished Postgraduate Scholars Pro-gramme. MJH acknowledges support from NSERC through a Discovery Grant. We are honored and grateful for the opportunity of observing the Universe from Maunakea and Haleakala, which both have cultural, historical and natural significance in Hawaii. This work is based on data obtained as part of the Canada-France Imaging Survey, a CFHT large program of the National Research Council of Canada and the French Centre National de la Recherche Scientifique. Based on observations obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA Saclay, at the Canada-France-Hawaii Telescope (CFHT) which is operated by the National Research Council (NRC) of Canada, the Institut National des Science de l’Univers (INSU) of the Centre National de la Recherche Scientifique (CNRS) of France, and the University of Hawaii. This research used the facilities of the Canadian Astronomy Data Centre operated by the National Research Council of Canada with the support of the Canadian Space Agency. This research is based in part on data collected at Subaru Telescope, which is operated by the National Astronomical Observatory of Japan. Pan-STARRS is a project of the Institute for Astronomy of the University of Hawaii, and is supported by the NASA SSO Near Earth Observation Program under grants 80NSSC18K0971, NNX14AM74G, NNX12AR65G, NNX13AQ47G, NNX08AR22G, 80NSSC21K1572 and by the State of Hawaii. This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos. esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This research made use of Photutils, an Astropy package for detection and photometry of astronomical sources (Bradley et al. 2024).