In secure web application development, the role of web services will not continue if it is not trustworthy. Retaining customers with applications is one of the major challenges if the services are not reliable and trustworthy. This article proposes a trust evaluation and decision model where the authors have defined indirect attribute, trust, calculated based on available direct attributes in quality web service (QWS) data sets. After getting training of such evaluation and decision strategies, developers and customers, both will use the knowledge and improve the QoS. This research provides web-based learning about web service quality which will be utilized for prediction, recommendation and the selection of trusted web services in the pool of web services available globally. In this research, the authors include designs to make decisions about the trustworthy web services based on classification, correlation, and curve fitting to improve trust in web service prediction. In order to empower the web services life cycle, they have developed a quality assessment model to incorporate a security and performance policy.