This study presents a framework for dynamic assortment selection and pricing using a censored multinomial logit choice model, where sellers can optimize product offerings and prices based on buyer preferences and valuations. By employing a Lower Confidence Bound pricing strategy alongside Upper Confidence Bound or Thompson Sampling approaches, the proposed algorithms achieve significant regret bounds, which are validated through simulations.