Extranuclear localization of long non-coding RNAs (lncRNAs) is poorly understood. Based on machine learning evaluations, we propose a lncRNA-mitochondrial interaction pathway where Polynucleotide Phosphorylase (PNPase), through domains that provide specificity for primary sequence and secondary structure, binds nuclear-encoded lncRNAs to facilitate mitochondrial import. Using FVB/NJ mouse and human cardiac tissues, RNA from isolated subcellular compartments (cytoplasmic and mitochondrial) and crosslinked immunoprecipitate (CLIP) with PNPase within the mitochondrion were sequenced on the Illumina HiSeq and MiSeq, respectively. LncRNA sequence and structure were evaluated through supervised (Classification and Regression Trees (CART) and Support Vector Machines, (SVM)) machine learning algorithms. In HL-1 cells, qPCR of PNPase CLIP knockout mutants (KH and S1) were performed. In vitro fluorescence assays assessed PNPase RNA binding capacity and verified with PNPase CLIP. 112 (mouse) and 1,548 (human) lncRNAs were identified in the mitochondrion with Malat1 being the most highly expressed. Most non-coding RNAs binding PNPase were lncRNAs, including Malat1. LncRNA fragments bound to PNPase compared against randomly generated sequences of similar length showed stratification with SVM and CART algorithms. The lncRNAs bound to PNPase were used to create a criterion for binding, with experimental validation revealing increased binding affinity of RNA designed to bind PNPase compared to control RNA. Binding of lncRNAs to PNPase was decreased through knockout of RNA binding domains KH and S1. In conclusion, sequence and secondary structural features identified by machine learning enhance the likelihood of nuclear-encoded lncRNAs to bind to PNPase and undergo import into the mitochondrion.