The booming volume of genomic data requires computational techniques that will allow the efficient extraction of biologically meaningful patterns. This work investigates advanced data mining algorithms for extracting frequent patterns and motifs in DNA and RNA sequences based on k-mer analysis. The nucleotide sequences are divided into shorter pieces called k-mers, where frequent pattern mining, positional frequency mapping, and dimensionality reduction techniques are applied to expose conserved motifs and structural characteristics. The proposed methodology employs Apriori for mining, followed by Principal Component Analysis (PCA) and a variation of a sequence logo style for visualization, which exposes latent relationships and biologically relevant sequence patterns. The experimental data show uniformly distributed occurrences of dominant k-mers and positionally clustered functional motifs like the “ATG” codon. The PCA projections in 2D and 3D space further highlight the structural diversity and possible clustering of sequences according to k-mer profiles. This research extends the frontier of bioinformatics by aiding in the interpretation of genomic sequences, with applications in genome annotation, disease-associated gene prediction, and regulatory motif identification. It serves the purpose of informing about the methods being brought together under k-mer analysis, frequent pattern mining, PCA, and motif visualization.