The current study devises an optimized ethanolic extraction for efficient recovery of high-value components from Pakistani olives (cv. Arbequina) using response surface methodology (RSM) and artificial neural networking (ANN). Four factors such as time, temperature, solvent concentration, and solute weight (g/100 mL) were evaluated as independent variables for determining the response (% yield). The results obtained under optimum extraction conditions such as duration (25 min), temperature (45 °C), solvent concentration (65 %; ethanol: water v/v), and solute (7.50 g/100 mL) offered bioactives extract yield of 40.96 % from Arbiquina olives. The analysis of variance (ANOVA) for the RSM model showed significant p-values and a correlation coefficient (R2) of 0.9960, confirming model's reliability. The results of ANN, which employed the multilayer perceptron design, were fairly in line with the findings of the experiments. The antioxidant characteristics and GC-MS metabolite profile of the obtained extracts were examined. Arbequina olive extract (AOE) demonstrated very good antioxidant ability in terms of total phenolic, total flavonoid contents, and DPPH radical scavenging. The GC-MS analysis of AOE confirmed the presence of several bioactives, including oleic acid (36.22 %), hydroxytyrosol (3.95 %), tyrosol (3.32 %), β-sitosterol (2.10 %), squalene (1.10 %), sinapic acid (0.67 %), α-tocopherol (0.66 %), vanillic acid (0.56 %), 3,5-di-tert-butylcatechol (0.31 %), and quercetin (0.21 %). The suggested optimized extraction method can be employed to efficiently extract a wide variety of high-value components from olives with potential for nutraceutical applications.