Artificial intelligence (AI) methods are successfully used to investigate several biological problems. The present survey of the literature is aimed at identifying the applications of AI methods (machine learning, deep learning, and computer vision) to symbiotic organisms including the associations of both macro and microorganisms. It was relevant to extend this review to applications in metagenomic and image data analysis, those being the dominant methods in the study of environmental impacts to symbiosis. The survey involved 161 papers from 2020-2021to draw conclusions. This analysis revealed that the application of AI methods is crucial for both metagenomic and image data (camera and remote sensing data) analysis for taxon identification, interaction, and phenotyping, and biomonitoring studies in almost all symbiotic associations from bacteria, lichens to coral reefs. The survey further revealed that AI methods have been adopted for inferring the environmental impacts because they can overcome some of the difficulties associated with modelling highly dimensional and nonlinear environmental data with complex interactions. These methods proved to be especially useful if the sample size was small, has mixed data types, or missing values and can be applied to reduce computation time. Different supervised and unsupervised machine learning algorithms such as random forest, support vector machines, k-mers, etc. are being applied at every step in metagenomic data analysis, from the taxonomic assignment, binning to gene and functional annotation, and are gradually replacing the manual methods with automated AI-based annotation. Similarly, the applications of deep learning and computer vision algorithms e.g., convolutional neural network are progressing steadily towards automation of image annotation in the monitoring of marine and terrestrial symbiosis. However, before we can use these methods to their full potential in understanding the multidimensional effects of environmental factors including the impact of anthropogenic activities, there are several gap areas. For example, the identification of novel and cryptic species, limited reference databases, and big data challenges, need to be addressed with further research. There needs to be better communication between biologists and machine learning research communities so that good quality labelled data is generated in future experiments.