Avian botulism caused by a bacterium, Clostridium botulinum, causes a paralytic disease in birds often leading to high fatality, and is usually diagnosed using molecular techniques. Diagnostic techniques for confirming the outbreak of Avian botulism include: Mouse Bioassay, ELISA, PCR (based on post-mortem), all of which are time-consuming, laborious and require invasive sample collection from affected sites or dead birds. All these in-vitro techniques are non-preventive and their processing time may further contribute to the bacterial growth in the affected regions. In this study, we build a first-ever multi-spectral, remote-sensing imagery based global Bird-Area Water-bodies Dataset (BAWD) (i.e. fused satellite images of warm-water lakes/marshy-lands or similar waterbody sites that are important for avian fauna) backed by on-ground reporting evidence of outbreaks. In the current version, BAWD consists of multi-spectral satellite images, covering a total ground area of 904 sq.km from two open source satellite projects (Sentinel and Landsat). BAWD consists of 17 topographically diverse global sites spanning across 4 continents, where locations were monitored over a time-span of 3 years (2016-2020). Using BAWD and state-of-the-art deep-learning techniques we propose a first-ever Artificial Intelligence based (AI) model to predict potential outbreak of Avian botulism called AVI-BoT (Aerosol Visible, Infra-red (NIR/SWIR) and Bands of Thermal). AVI-BoT uses fused multi-spectral satellite images of water-bodies (10-bands) as input to generate a spatial prediction map depicting probability of potential Avian botulism outbreaks. We also train and investigate a simpler (5-band) Causative-Factor model (based on prominent physiological factors reported in literature as conducive for outbreak) to predict Avian botulism. Using AVI-BoT, we achieve a training accuracy of 0.94 and validation accuracy of 0.96 on BAWD, far superior in comparison to our model based on causative factors. For detailed feature-space analysis and explain-ability of the proposed 10-band AVI-BoT model, we further train four additional spectral models on BAWD using: (i) Aerosol, (ii) Visible (3-bands), (iii) IR (3-bands), and (iv) Thermal (3bands). Further, we analyze using AVI-BoT, two recent and highly complex outbreak case-studies: (i) Sambhar Lake (India, November 2019), and (ii) Klamath National Wildlife Refuge (U.S.A., October 2020). The model is found to closely converge with ground-reporting observations at both these sites with a fine-grain spatio-temporal prediction capability. The proposed technique presents a scale-able, low-cost, non-invasive methodology for continuous monitoring of bird-habitats against botulism outbreaks with the potential of saving valuable fauna lives.