Anesthetics revolutionized modern medicine by enabling life-saving invasive procedures. Their profound impact has not only advanced clinical practice, but also catalyzed basic neuroscience research. Anesthetics are effective across all living organisms, from bacteria to humans. However, despite being extensively used since their discovery two centuries ago, the mechanisms by which anesthetics produce their desirable hypnotic effects remain incompletely understood. In this dissertation, I employ several complementary methodologies to deepen our understanding of the mechanisms underlying anesthetic action. First, I apply mathematical modeling to characterize individualized spontaneous arousal state transitions. I produce a probabilistic state transition model that can explain why brain states fluctuate despite constant anesthetic exposure. This model was validated across different drugs, concentrations, and species while also providing directly testable predictions which may deliver additional insight on brain state transitions between wakefulness and unresponsiveness. My second advance provides a mechanistic explanation to why anesthetic sensitivity appears to be sexually dimorphic across species. I leverage comprehensive behavioral assessments, bidirectional hormonal modulation, electroencephalographic analysis, and single cell whole brain activity mapping to corroborate clinical evidence of sex differences in anesthetic sensitivity. I uncover that the female brain is evolutionarily more resistant to anesthetics, primarily due to fundamental hormonal differences. I also find that assessment of anesthetic depth using EEG measures fail to show sex differences while differential activity of sleep- and anesthetic-promoting hypothalamic nuclei correlate to degree of anesthetic sensitivity. My third advance in elucidating mechanisms of anesthesia stems from big data driven discovery of an isoflurane anesthesia network. This network poses a critical link to how molecularly distinct anesthetics create a common hypnotic endpoint, in part through interaction with endogenous sleep circuitry. I discover a new prefrontal cortical anesthetic promoting brain region and confirm the newly proposed network in one anesthetic using viral gene transcription, neuroanatomic tracing, and chemogenetic modulation techniques. Altogether, the results of this thesis deliver foundational evidence how brain state transitions can be modelled, provide data driven evidence on how to improve clinical guidance of anesthetic management, and advance our current understanding on mechanisms of anesthetic induced hypnosis.