Direct Block Scheduling (DBS) has become an essential tool in modern mine planning. It is particularly important in open pit optimisation because it can ensure an optimal net present value (NPV) by modelling intricate mining systems. DBS maximises NPV by leveraging dynamic cut-off grades, blending constraints, and capital expenditure options. This paper presents the application of DBS to integrate the Bienenstock Zuckerberg (BZ) algorithm with mixed-integer linear programming (MILP) and clustering algorithms for generating mining phases. This integration offers a highly efficient solution to complex pit optimisation challenges. DBS excels in modelling multi-mine and multi-block model systems. The method offers a significant advantage in strategic planning, where ore blending strategies can incorporate multiple pits or regions. The early-stage integration of blending constraints in DBS ensures that ore quality is maintained, enhancing downstream processing efficiency. In addition, the ability to strategically manage capital expenditure phases over time makes DBS a powerful tool for long-term financial planning in mining projects. The BZ algorithm enhances DBS by allowing it to solve complex scheduling problems within a reasonable time frame. The reduced time required to solve complex linear relaxations is a substantial improvement over the traditional MILP method. The computational efficiency of BZ algorithm allows for the generation of high-quality, optimised strategic mining strategy that aligns with both operational and financial objectives, even in large-scale mining environments. The use of a clustering algorithm to augment DBS offers significant strategic advantages for schedule optimisation. It does this by grouping similar blocks based on DBS period, ore quality, proximity, or operational characteristics. This clustering of mining tasks into phases/pushbacks transitions the optimised mining sequence into an efficient mining production schedule. This reduces the size of the model to be solved. This paper discusses the combined use of DBS (with the BZ algorithm) and clustering techniques. It demonstrates the collective impact on optimising pit designs, maximising NPV, managing capital expenditures, and enhancing operational profitability. The current approach offers superior results for mining operations that need a comprehensive solution to the challenge of producing an optimised schedule for complex mining operations.