Bioacoustics, or the study of sound in biological contexts, has emerged as a point of great interest in helping further understand species behavior, biodiversity, and ecosystem dynamics. Traditional bioacoustic analysis relies on single-modality features, focusing mostly on audio. However, ecology is a complex process that requires more systems thinking. The integration of bioacoustic data with multiple other modalities-such as environmental variables, visual imaging, and behavioral metrics-carries transformative potential. This chapter explores bioacoustic data fusion and multimodal analysis as a comprehensive approach to ecological research. Recent developments in sensor technology, machine learning, and big data analytics have been able to make possible the collection, integration, and interpretation of diverse datasets. Bioacoustic data fusion takes advantage of this synergy, adding complementary modalities to acoustic signals in order to enhance the resolution and context of ecological insights. Applications range from monitoring animal populations to the detection of endangered species and evaluation of the impacts of climate change on ecosystems. Some of the important challenges that have arisen relate to data heterogeneity, synchronization, and computational scalability. Recent advances using techniques such as deep learning models, graph-based methods, and cloud-based platforms have overcome these challenges to pave the way toward real-time, scalable systems. This chapter further demonstrates case studies that reveal the possibility of integrating multiple modes of bioacoustics into practical applications: from mapping sounds to habitat to linking calls with physiological states. Bioacoustic data fusion and its multivariate analyses have great potential to bring large changes in ecological science and in conservation through promoting collaborations within and outside disciplines using leading-edge analytics frameworks. This chapter serves to provide a guide on state-of-the-art techniques, applications, and future directions in this dynamic field.