The deconvolution of contributors from mixed biological samples represents a long-standing core technical challenge in the field of forensic genetics, as the accuracy of this process directly impacts case investigation efficiency and judicial fairness. Based on whether the deconvolution relies on software as a boundary, this paper systematically reviews the two existing types of strategies for deconvoluting mixed biological samples. (1) Pre-processing strategies based on biological experiments, which achieve physical separation and independent detection of cells from different sources through microscope-guided single-cell isolation, or accomplish differentiation prior to detection via specific physicochemical treatments. Among these, physical separation based on differences in cell morphology mainly employs cell capture techniques characterized by low-to-medium throughput but high precision, using visualization tools such as microscopes for single-cell isolation, followed by specialized detection of the isolated cells. Chemical separation based on differences in biochemical properties mainly targets different body fluids and can directly link a body fluid to its contributing source, including differential lysis, as well as coding-region single nucleotide polymorphisms (cSNPs) and some compound markers. (2) post-processing strategies based on computational biology, which rely on bioinformatics algorithms to analyze mixed DNA testing data, enabling precise discrimination of contributor components. Among these, deconvolution based on probabilistic models mainly depends on probabilistic genotyping software (PGS), currently including software that applies two core algorithms: Markov chain Monte Carlo methods (MCMC) and maximum likelihood estimation (MLE). While clustering-based deconvolution relies on single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq), in which each cell is labeled with a distinct barcode, thereby allowing deconvolution of the resulting data and classification of each cell into its cell population of origin. On this basis, this paper summarizes the academic debates, advantages, and disadvantages of the two deconvolution strategies, evaluates their applicable scenarios and limitations in forensic practice, and proposes a graded application strategy based on sample quality assessment, as well as a pathway for the integrated use of the different deconvolution strategies, taking into account the diverse conditions of forensic exhibits and limited resources in routine forensic casework: for sperm-vaginal fluid mixed stains, differential lysis is preferentially adopted to obtain single-source male and female profiles. If same-sex contributors are involved, PGS is further used for deconvolution. For mixed samples that are conventional in amount and well preserved, PGS can be used for post-processing deconvolution after routine STR profiling. For mixed samples that are trace, aged, and poorly preserved, it is necessary to introduce linear amplification via transposon insertion (LIANTI) or primary template-directed amplification (PTA) for whole genome amplification (WGA) on the basis of isolated cells, thereby creating conditions for obtaining STR profiles. Finally, this paper looks forward to the future development of rapid testing instruments for mixed forensic samples and the promising applications of artificial intelligence in mixture deconvolution: the former includes the integration of microfluidic chips, the field deployment of portable nanopore sequencing and its combined use with PGS, while the latter includes the application of artificial intelligence technologies such as deep learning models and multimodal large language models, aiming to provide a methodological reference for the accurate deconvolution of mixed biological samples.