Bias within Artificial Intelligence (AI) systems constitutes a profound challenge with substantial implications for fairness, accountability, and societal equity. This paper presents an exhaustive examination of the ontology of bias in AI, delving deeply into its conceptual underpinnings and exploring the intricate algorithmic consequences arising from biased data and models. By establishing a comprehensive and nuanced framework that categorizes diverse manifestations of bias and elucidates their origins, this study aims to foster a profound understanding of how bias permeates AI systems. Integrating interdisciplinary perspectives drawn from philosophy, sociology, and computer science, the ontology of bias is meticulously dissected to reveal its multifaceted nature. Furthermore, the paper investigates the profound impacts of these biases on critical decision-making processes and proposes multifaceted strategies for mitigating bias through ethical design, advanced algorithmic techniques, and stringent regulatory frameworks. Through detailed case studies and empirical analysis, this research highlights the inherent complexities in addressing bias and underscores the imperative for collaborative endeavors to cultivate equitable AI technologies.