A robust strategy for recognition and prediction of gait events using wearable sensors is presented in this paper. The strategy adopted here uses a combination of two computational intelligence approaches: Adaptive Neuro-Fuzzy and Bayesian methods. Recognition of gait events is performed by a Bayesian method which iteratively accumulates evidence to reduce uncertainty from sensor measurements. Prediction of gait events is based on the observation of decisions and actions made over time by our perception system. An Adaptive Neuro-Fuzzy system evaluates the reliability of predictions, learns a weighting parameter and controls the amount of predicted information to be used by our Bayesian method. Thus, this strategy ensures the achievement of better recognition and prediction performance in both accuracy and speed. The methods are validated with experiments for recognition and prediction of gait events with different walking activities, using data from wearable sensors attached to lower limbs of participants. Overall, results show the benefits of our combined Adaptive Neuro-Fuzzy and Bayesian strategy to achieve fast and accurate decisions, but also to evaluate and adapt its own performance, making it suitable for the development of intelligent assistive and rehabilitation robots.