Abstract Long noncoding RNA (lncRNA)–protein Interaction (LPI) across diverse biological systems, directly and indirectly, regulates various cellular processes. Experimental assays to recognize the protein binding partners of lncRNAs are highly time-consuming and expensive. In silico predictive approaches involving pattern recognition techniques provide a promising alternative to it by reducing the search space. Our work identifies such hidden patterns within the cross-linking immunoprecipitation sequencing (CLIP-Seq) data, which aid in overcoming the problem of obtaining a real negative dataset and thus offer a state-of-the-art machine learning (ML)–based prediction algorithm to predict LPI. The initial phase of this work involves preparing the training dataset, and the next phase is devoted towards developing an ML-based model to perform prediction operations. To demonstrate the efficacy of our model, its performance has been compared to that of contemporary prediction tools, with the result clearly showing the outperformance of our model. Moreover, it also provides the segments of interaction within the lncRNA loci, which act as a roadmap for the precise design of the validation experiment. The LncPTPred tool has been provided in terms of a web server as well as a standalone version on GitHub. Web server Link: http://bicresources.jcbose.ac.in/zhumur/lncptpred/. Github Link: https://github.com/zglabDIB/lncptpred.git.