The human microbiota is a central regulator of cancer development and response to treatment. Once believed to be incidental the complex interaction between immune system and host associated microbial communities is now recognized as a critical component of oncogenesis and treatment modulation. This chapter explores how microbial dysbiosis causes immunological dysfunction, metabolic abnormalities, and chronic inflammation which in turn contribute to the development of cancer. It focus mainly on microbiota associated cancers such as colorectal, gastric and hepato-cellular cancers. It also discusses how the microbiota influences immunological responses, drug metabolism, and systemic inflammation all of which have an effect on the efficacy and toxicity of cancer treatments like immunotherapy, chemotherapy and radiation. But despite significant progress, there are still significant gaps in our mechanistic understanding of host microbiota interactions in cancer therapy. Current studies are frequently limited by interindividual variability, lack of longitudinal data, and inconsistent microbiota analysis methodologies. Moreover, microbiota profiling in oncology is not sufficiently clinically integrated and the effects of microbial modulation strategies remain poorly understood. This chapter presents new information from preclinical and clinical studies that link specific microbial taxa and metabolites to the outcomes of treatment responses in a novel manner. It also highlights novel therapeutic strategies such as dietary modifications, targeted probiotics, and fecal microbiota transplantation as potential adjuncts to conventional cancer treatment. Moreover, a promising strategy to enhance therapeutic efficacy and lessen side effects is sensible microbiota modulation based on the patient’s microbial signature. In order to fully utilize the therapeutic potential of the human microbiome in cancer care, the chapter ends by highlighting the necessity of multidisciplinary research projects combining oncology, microbiology, and computational biology.