Cigarette smoking is the most prevalent cause of preventable deaths in the whole world. There are hundreds of toxins in a single cigarette that can have harmful effects on both active and passive smokers. Researchers think it is important to understand and provide accurate information regarding daily cigarette smoking and smoke exposure to understand the health impacts of cigarette consumption. There are many tools available for estimation of daily cigarette consumption such as self-report, biomarkers of cigarette smoke exposure, puff topography devices, and, recently, wearable sensors. However, these methods have few limitations, such as recall biases or digit preference in self-reporting. Biomarkers are objective and accurate, but they are expensive and not feasible for monitoring daily consumption. Puff-topography devices can provide puffing and cigarette consumption information but fail to report the post-puff information, such as duration of cigarette smoke holding in the lungs. Research shows wearable sensors can objectively and automatically detect cigarette smoking in the free-living environment. However, they are limited to detecting the number of cigarettes, the number of puffs, duration of puff, or duration of cigarette smoking. None of the methods available to date can identify the post-puff information such as depth of inhalation, smoke holding duration, etc. This information is vital in understanding the detailed smoking behavior of an individual smoker. Thus, there was a need for the development of a reliable method for extracting the puffing and post-puffing information of daily cigarette consumption of individual smoker. This dissertation proposes the use of breathing signal for extracting smoke exposure metrics. This dissertation also proposes the development of deep learning architecture for monitoring cigarette smoking in free-living; and signal processing/pattern recognition methods for extracting post-puff information. The main accomplishments of this dissertation are (a) review of existing methods for monitoring cigarette smoking and measurement of cigarette smoke exposure; (b) development of a novel algorithm named RSEM (Respiratory Smoke Exposure Metrics) for extracting puffing and post-puffing information ( such as puff duration, inhale-exhale duration and volume, volume over time, smoke hold duration, inter-puff interval) from breathing signal. The proposed algorithm provided measures previously unavailable in research; (c) establishing a relationship between smoke exposure metrics computed using RSEM algorithm and the biomarkers of smoke exposure expire CO and Cotinine level; (d) development of DeepPuff algorithm for automatic identification of smoking inhalation in the free-living environment; (e) analysis of cigarette smoking in the free-living environment and the effects of using puff topography devices on the number of puffs, smoking duration, puff duration, inhale-exhale duration, inhale-exhale volume, smoke hold duration, and inter-puff interval.