Modern computing systems must balance storage capacity against data retrieval speed under rapid data growth and strict performance requirements. The space-time trade-off principle holds that lower access latency usually requires additional storage overhead, whereas reducing the storage footprint increases computational cost. This study examines how contemporary storage techniques operationalize this trade-off and proposes a workload-aware framework for selecting among them. A structured qualitative review was conducted of 24 sources, comprising foundational works, peer-reviewed studies (primarily 2014-2026), and one standards document, using documented search strings and inclusion and exclusion criteria. The review covered indexing, caching, compression, deduplication, replication, erasure coding, log-structured storage, tiered architectures, and machine learning-driven tuning. Nine technique families were rated for space efficiency and access speed on a five-level ordinal scale with an explicit criterion for each level, and the ratings were supplemented with figures reported in the cited studies; for example, erasure-coded stores report storage overheads of 1.33x to 2.1x, against 3x to 3.6x for replication. The techniques were then mapped to four workload parameters: read/write ratio, access pattern, data temperature, and availability requirements. The synthesis indicates that no single technique simultaneously optimizes space efficiency and access speed, and that adaptive mechanisms such as tiered storage and learned tuning allow systems to reposition themselves as workloads change. The study contributes a decision framework consisting of rating criteria, a technique-rating table, a table of reported quantitative evidence, a workload-to-strategy matrix that incorporates energy, reliability, security, and cost checks, two worked scenarios, and a trace-based validation protocol. No new benchmarks are reported; executing the validation protocol is left for future work.