Current blood pressure (BP) estimation methods have not achieved an accurate and adaptable approach for application in populations at risk of cardiovascular disease, with generally limited sample sizes. Here, we introduce an algorithm for BP estimation solely reliant on photoplethysmography (PPG) signals and demographic features. Our approach automatically obtains signal features and employs the Markov Blanket (MB) feature selection to discern informative and transmissible features, achieving a robust space adaptable to the population shift. We validated our approach with the Aurora-BP database, compromising ambulatory wearable cuffless BP measurements for over 500 individuals. By evaluating several machine-learning regression methods, Gradient Boosting emerged as the most effective. The comparative assessment encompassed both a generic model (trained on unclassified BP data) and specialized models (tailored to each distinct BP population), with the former demonstrating consistent superiority with MAE  of 10.2 mmHg (0.28) for systolic BP and 6.7 mmHg (0.18) for diastolic BP on the whole dataset. Moreover, a comparison of in-clinic and ambulatory model performance showed a significant decrease in accuracy for the latter of 2.85 mmHg in systolic (p < 0.0001, F-value = 32764.76) and 2.82 mmHg for diastolic (p < 0.0001, F-value = 65675.36) estimation errors. Our work contributes to a resilient BP estimation algorithm from PPG signals, underscoring the advantages of causal feature selection and quantifying the disparities between ambulatory and in-clinic measurements.