Lymphomas exhibit considerable genomic complexity and variability, often resulting in differential responses to treatment and variable clinical outcomes. Mutational signatures—distinct patterns of somatic mutations caused by endogenous or exogenous processes—offer critical insights into the underlying mechanisms of lymphomagenesis. These signatures, derived from whole-genome or exome sequencing data, not only reflect the history of DNA damage and repair but also identify actionable pathways that may be amenable to targeted therapies. For instance, the presence of aberrant somatic hypermutation or activation-induced cytidine deaminase (AID)-related signatures is frequently observed in B-cell lymphomas and can influence response to immunochemotherapy or checkpoint inhibitors. Clonality assessments further enhance personalized medicine approaches by determining the evolutionary relationships and dominance hierarchies of tumor subclones. Understanding whether certain driver mutations are clonal (present in all tumor cells) or subclonal (restricted to subsets) can guide the selection and sequencing of targeted therapies. Clonal mutations in pathways such as B-cell receptor signaling, NF-?B, or JAK/STAT may predict robust responses to specific inhibitors, whereas subclonal alterations may require combination regimens or surveillance strategies. Integration of mutational signatures and clonal architecture is especially crucial for identifying early events in tumorigenesis versus late-arising resistance mutations. Advancements in high-throughput sequencing and computational modeling are enabling more precise reconstruction of lymphoma evolution and therapeutic vulnerabilities. Incorporating these molecular metrics into clinical decision-making may improve prognostication, reduce overtreatment, and enable dynamic adaptation of treatment regimens. This review highlights the synergistic utility of mutational signatures and clonality assessments in tailoring precision therapies and overcoming resistance in lymphoma care.