In the last century, cosmological and astrophysical observations indicated that ~ 85% of the matter in the universe is inert, non-baryonic and non-luminous dark matter. The existence of particle dark matter is well motivated, however, all that is known about its nature is indirect information coming from large-scale astrophysical/cosmological observations or laboratory bounds. The nature of this elusive yet abundant dark matter naturally became one of the greatest conundrums in modern science. A tremendous effort was spent on exploring the theory space and building detection experiments. Still, the dark matter remained elusive. This elusiveness emphasized the importance of a more comprehensive approach to the dark matter hunt: The standard minimalist scenarios may not be sufficient; wider mass ranges and richer ``dark sectors'' may need to be considered. The dark sector theories' potential to address the dark matter problem along with other problems in the Standard Model, and the independent motivation for them coming from various top-down models, caused them to get significant traction. In addition to exploring a broad range of theoretical possibilities, another important aspect of dark matter detection is background reduction. Since the dark matter particles are competing with the Standard Model particles for interacting with the experiments, the methods distinguishing the dark matter events are crucial to improve the signal-to-noise ratio. In this dissertation, we have embraced a comprehensive approach to dark matter detection by considering a wide range of dark matter candidates (with large freedom in mass, and considering nonminimal dark sectors), interactions (including nuclear recoils, collective excitations, electron excitations), and targets (including superfluids, superconductors, Dirac materials, polar crystals.) In addition to overall reach constraints, we also focused on the anisotropies and the daily modulation prospects to explore the potential to distinguish dark matter signals from the background noise effectively.