A novel valorization strategy is proposed in this work for the sustainable utilization of a major mango processing waste (i.e. mango seed kernel, MSK), integrating green pressurized-liquid extraction (PLE), bioactive assays and comprehensive HRMS-based phytochemical characterization to obtain bioactive-rich fractions with high antioxidant capacity and antiproliferative activity against human colon cancer cells. Thus, a two steps PLE procedure was proposed to recover first the non-polar fraction (fatty acids and lipids) and second the polar fraction (polyphenols). Efficient selection of the most suitable solvent for the second PLE step (ethanol/ethyl acetate mixture) was based on the Hansen solubility parameters (HSP) approach. A comprehensive GC-and LC-Q-TOF-MS/MS profiling analysis allowed the complete 36 characterization of the lipidic and phenolic fractions obtained under optimal condition (100% 37 EtOH at 150°C), demonstrating the abundance of oleic and stearic acids, as well as bioactive 38 xanthones, phenolic acids, flavonoids, gallate derivatives and gallotannins. The obtained MSK-39 extract exhibited higher antiproliferative activity against human colon adenocarcinoma cell line 40 HT-29 compared to traditional extraction procedures described in literature for MSK utilization 41 (e.g. Soxhlet), demonstrating the great potential of the proposed valorization strategy as a 42 valuable opportunity for mango processing industry to deliver a value-added product to the market with health promoting properties. , , , and , are regression 163 coefficients of variables for intercept, linear, quadratic, and interaction terms, respectively, and 164 and are the independent variables, representing solvent composition and temperature, 165 respectively. The adequacy of the model was determined by coefficient of regression (R 2) and the F-test value obtained from the analysis of variance (ANOVA) by the statistical software 167 STATISTICA 12 (Stat Soft, Inc., Tulsa, OK 74104, USA). Pareto charts for the standardized 168 effects of independent variables on response factors were also generated. A multiple response 169 optimization was carried out by combining the experimental factors, looking for maximizing 170 the desirability function (Ballesteros-Vivas et al., 2019).