Soft and glassy materials are ubiquitous in nature and everyday life, from a pile of sand, paints, foams, and cosmetics to cells and tissues. While these several seemingly different materials behave in and exhibit similar properties, many of their physical properties and associated phenomena have eluded understanding for a long time. In this thesis, I seek to study two major scientific problems through the energy landscape perspective -- understanding the complex phenomena and rheological similarity in soft glassy materials (SGMs), and developing in silico methods to sample low-energy configurations in glassy systems. SGMs constitute of several seemingly different soft materials, such as foams, cells, and many complex fluids, and exhibit glassy phase-like behavior. More remarkably, they display similar rheological properties and super-diffusive dynamics, termed soft glassy mechanics. Here, we show that such behavior in ripening foams is a direct consequence of their energy landscapes and their stratified organization. A simple bubble model of a damped ripening foam at sufficiently weak damping, hops along a path containing various minima on such a landscape. In particular, we observe intermittent heavy-tailed dynamics, bubble super-diffusion, and power-law rheology in the low viscosity limit. For strong damping, the viscous stresses cause the system configuration to evolve along higher energy paths, washing out small-scale tortuosity and producing motion with an increasingly ballistic character. Using a microrheological approach, the linear viscoelastic response of the model is efficiently calculated. This resembles the power-law rheology expected for soft glassy mechanics and is nearly insensitive to the damping parameter, before switching to an elastic response for sufficiently strong damping. Intermediate viscosities, interestingly continue to produce power-law rheology and fractal motion without intermittency. Lastly, we study the reported memory effect in foams after large perturbations and find that the memory time scale goes to zero as the damping parameter vanishes with no dependence on ripening, suggesting that the time scale is a viscosity-dominated phenomenon rather than being due to slow structural changes activated by rearrangements. Overall, we present a purely landscape paths based picture for so-called soft-glassy mechanics without invoking any classical theories based on glassy distribution of minima. Our findings suggest a stratified energy landscape where distinct domains give rise to separate foam-like, intermittent, and glass-like dynamics. Next, we switch our attention to other glass-forming systems and part ways with ripening-driven mechanics, switching to artificially biased algorithmic approaches. We report that a modified metadynamics algorithm efficiently explores and samples low-energy regions of such high-dimensional landscapes. Piloting on the previously studied energy landscape of a model foam, our algorithm finds and descends meandering `canyons' in the landscape, which contain dense clusters of energy minima along their floors. Similar canyon structures in the energy landscapes of two model glass formers -- hard sphere fluids and the Kob-Andersen glass -- allow us to reach high densities and low energies, respectively. In the hard-sphere system, fluid configurations are found to form continuous regions that cover the canyon floors up to densities well above the jamming transition. For the Kob-Andersen glass former, our technique samples low energy states with modest computational effort, with the lowest energies found approaching the predicted Kauzmann limit. We further characterize the lengthscales associated with these canyons by clustering minima along their bottoms. We find that these canyons are low-dimensional (~2.4) structures embedded in the high-dimensional energy landscape. This remarkable numerical discovery of 'canyons' enables our algorithmic approach to find low-energy glassy states and will stimulate new accelerated simulation approaches in glasses and perhaps related fields such as deep learning.